Decentralized Machine Learning Networks: An Integrated Approach

Abstract
Decentralized Machine Learning Networks (DMLN) represent a revolutionary paradigm that integrates distributed computing, machine learning, and blockchain technology to create scalable, fault-tolerant, and secure systems for handling complex tasks across networks of devices. This comprehensive paper examines the architecture, operational mechanisms, challenges, and innovative solutions associated with DMLN. We explore how these networks employ specialized models for resource management, error-correction, security, and human-computer interaction to overcome traditional machine learning limitations while enhancing data privacy and system resilience. Through a detailed analysis of current implementations and future directions, this paper demonstrates how DMLN are transforming data processing and analysis methodologies across multiple domains including healthcare, finance, transportation, and telecommunications.

Introduction to Decentralized Machine Learning Networks

Decentralized machine learning has emerged as a promising paradigm for addressing limitations in traditional centralized approaches to machine learning. By leveraging distributed computing infrastructures, machine learning algorithms, and blockchain technology, DMLN offer a framework that eliminates central server bottlenecks through peer-to-peer message passing between neighboring nodes. This decentralized approach facilitates collaborative model training without sharing raw data, addressing critical issues such as data privacy, security, access rights, and heterogeneous data utilization.

The technological foundations of DMLN include consensus-based optimization algorithms that enable network nodes to cooperatively minimize locally available cost functions through local computation and communication. These networks employ various state-of-the-art formulations, consensus-based procedures, and decentralized counterparts of centralized stochastic first-order methods to achieve efficient and secure distributed learning. The resulting systems are communication-efficient, exploit data parallelism, demonstrate resilience in challenging environments, and remain simple to implement, providing scalable solutions for large-scale machine learning problems.

DMLN Architecture and Components

Distributed Computing Infrastructure

The distributed computing infrastructure of DMLN comprises diverse devices including smartphones, tablets, laptops, and edge devices that contribute processing power and resources in a decentralized manner. This network topology supports peer-to-peer communication protocols that improve resilience and counteract service degradation, enabling peers to evaluate protocol effectiveness and distribute limited resources like bandwidth to match application requirements. The architecture allows data to remain localized while still contributing to global model improvements, addressing both privacy concerns and computational efficiency.

In practical implementations, these infrastructures often employ overlay networks to improve communication efficiency in bandwidth-limited environments. Recent advances in network tomography have enabled the joint design of communication demands and schedules for overlay-based DMLN, minimizing total training time without requiring explicit cooperation from underlying network infrastructure. This approach has demonstrated significant acceleration of decentralized learning compared with state-of-the-art designs.

Machine Learning Models and Their Specialization

DMLN employ specialized models for different system functions, creating a robust architecture that addresses specific operational requirements. Core algorithms include decentralized stochastic first-order methods that facilitate machine learning from decentralized features, enabling collaborative training across distributed parties without sharing original data. This preserves data confidentiality while still achieving effective model training.

For resource management, DMLN incorporate dedicated models that optimize task distribution across devices with varying capabilities. Advanced frameworks utilize Graph Neural Networks to embed resource and application states, enabling comprehensive representation and efficient decision-making through collaborative multi-agent reinforcement learning. These systems optimize local neighborhoods while maintaining system-wide coordination through global orchestration.

Error-correction models employ both proactive and reactive mechanisms to ensure system reliability. Forward error correction techniques reconstruct lost or corrupted packets, improving performance over unreliable links. Machine learning approaches like Convolutional Neural Networks and Recurrent Neural Networks can detect and correct errors in real-time, enhancing overall system robustness.

Security models implement multi-layered approaches to protect against both external and internal threats. Effective implementations include three-layer security models that combine blockchain technology for trusted user authentication, trusted execution environments for user privacy, and detection algorithms to defend against internal attackers.

Human-computer interaction models facilitate seamless communication between users and the network. Multimodal perceptual systems using deep neural networks for acoustic feature extraction and image feature fusion enable more intuitive and responsive interactions. Advanced frameworks like COGNET provide context-sensitive decision structuring and task automation for complex real-time applications.

Blockchain-based Data System

Blockchain technology provides the foundation for data integrity, decentralization, and fault tolerance in DMLN. This technology ensures traceability and immutability of model updates, with each update associated with specific participants. Such traceability enables detection of fraudulent transactions that attempt to tamper with data, while decentralization strengthens overall system fault tolerance and attack resistance.

In blockchain-integrated machine learning systems, smart contracts automate secure data exchanges and model updates, validating transactions and enhancing transparency. Various consensus mechanisms including Practical Byzantine Fault Tolerance (PBFT) and Proof of Authority (PoA) secure data validation across the network. These mechanisms have demonstrated exceptional fault tolerance in experiments, showing how blockchain-based networks substantially outperform centralized and cryptographic method-based networks in terms of resilience.

Applications of Decentralized Machine Learning Networks

Healthcare Applications

DMLN have demonstrated significant potential in healthcare systems where data privacy and security are paramount concerns. Implementations combining blockchain and machine learning enable anomaly detection in time-series data with high precision, while smart contracts allow automated responses to threats such as compromised nodes. These systems have shown over 95% detection accuracy and 30% reduction in response times compared to traditional approaches.

For medical data management, DMLN architectures using Ethereum smart contracts and interplanetary file systems provide secure storage and validation through consensus algorithms. This approach ensures data integrity while reducing unwanted tampering risks by recording all data storage and access transactions in an immutable manner. The resulting systems demonstrate 90% accuracy rates compared to 75% in centralized systems.

Financial Systems

In financial applications, DMLN enable enhanced security and trading strategies in decentralized finance (DeFi) platforms. Machine learning algorithms analyze large volumes of transaction data to identify patterns and improve market efficiency. Specific implementations like Compound, Aave, and MakerDAO have successfully integrated machine learning for risk management, liquidity provision, and improved collateral management.

Blockchain-based DMLN in banking systems enhance security against intruders through integration with tools like MetaMask and Ganache, while machine learning algorithms detect illegal transactions. These systems significantly mitigate fraud, streamline operations, and enhance data integrity despite challenges in scalability and regulatory compliance.

Transportation and Energy Networks

In vehicular networks, DMLN enable secure, transparent, and scalable Vehicle-to-Everything (V2X) communication systems. These implementations leverage blockchain for secure data exchange among vehicles, infrastructure, and edge devices, while machine learning algorithms enhance predictive capabilities for route optimization, collision avoidance, and traffic management. Experimental evaluations demonstrate significant improvements in network performance, security metrics, and decision-making accuracy compared to traditional frameworks.

For energy management, DMLN provide transparent, secure energy transactions through blockchain's decentralized, immutable ledger system. Smart contracts automate and ensure trust in energy trades, addressing critical security and transparency issues in increasingly decentralized energy systems. These implementations have shown particular effectiveness in microgrid energy management using alternative distributed ledger technologies like IOTA Tangle.

Challenges and Solutions in DMLN Implementation

Scalability and Latency Challenges

DMLN face significant scalability challenges related to communication overhead, resource constraints, and diverse client populations. Blockchain technologies in particular encounter scalability limitations due to consensus mechanisms like Proof of Work (PoW), which restrict transaction throughput and increase latency. These challenges intensify in bandwidth-limited networks where traditional DMLN designs make unrealistic assumptions about physical network adjacency.

Several innovative solutions have emerged to address these challenges. Alternative distributed ledger technologies such as Directed Acyclic Graphs (DAGs) offer higher scalability and energy efficiency than traditional blockchains by structuring transactions and blocks as a DAG, enabling fast validation without compromising security. Experimental results show DAG-based models minimize end-to-end delay, time cost, energy consumption, and improve throughput compared to classic blockchain systems.

More efficient consensus mechanisms like Proof of Stake (PoS) and Delegated Proof of Stake (DPoS) significantly reduce energy consumption, transaction costs, and confirmation speeds compared to Proof of Work systems. These mechanisms achieve this by replacing computational puzzle-solving with validator stake commitments. For DMLN specifically, communication-efficient aggregation techniques and adaptive client sampling strategies have proven effective at mitigating overhead while maintaining model quality.

Resource Management Optimization

Effective resource management remains crucial for DMLN performance, particularly when addressing heterogeneous device capabilities and network conditions. Decentralized approaches must adapt to communication resource constraints to prevent performance degradation in practical scenarios. The challenge extends to ensuring efficient bandwidth utilization while maintaining model convergence.

Advanced solutions include adaptive compression mechanisms that adjust transmission data compression ratios to keep communication latency within constraints. Network topology pruning approaches reduce communication overhead by eliminating poor-quality links while maintaining convergence guarantees. Power allocation strategies further improve performance by intelligently redistributing communication energy while complying with resource constraints.

Hardware-assisted decentralized resource management strategies offer another promising approach. These systems define network regions through reconfigurable resource management policies, demonstrating improved performance and communication resource allocation with minimal area overhead. For cloud computing environments, VM-based resource management approaches can dynamically balance loads across physical nodes while reducing overall power consumption.

Security and Privacy Protection

DMLN face significant security challenges including vulnerability to poisoning attacks, Byzantine faults, and privacy breaches. External attacks from malicious actors can compromise model integrity, while internal threats from compromised nodes present equally serious risks. Traditional centralized approaches often fail to address these vulnerabilities.

Multi-layered security models have proven effective at mitigating these threats. Blockchain-based authentication ensures only trusted users access the network, trusted execution environments protect local user privacy, and advanced detection algorithms identify internal attackers should perimeter defenses fail. Experimental results demonstrate these combined approaches effectively defend against both external and internal attacks.

For privacy preservation specifically, cryptographic techniques including multi-key homomorphic encryption allow machine learning training on encrypted data. This enables prediction of outcomes like peak power loads without compromising user data privacy. Blockchain-enhanced privacy mechanisms further protect sensitive information through selective disclosure and zero-knowledge proofs.

Storage and Computation Overhead Management

Storage and computation overhead present significant challenges for DMLN, especially when handling large-scale models and datasets across resource-constrained devices. In virtual machine environments, duplicate data across volumes creates unnecessary redundancy. Additionally, synchronous operation requirements can introduce substantial processing delays.

Efficient deduplication systems offer substantial improvements by reclaiming duplicates while maintaining performance. Fully decentralized systems performing cluster-wide off-line deduplication of virtual machines' primary volumes can operate on any basic shared block device interface. These approaches achieve minimal I/O overhead even when deduplication and intensive storage operations execute simultaneously.

For computational efficiency, reinforcement learning-driven approaches to resource allocation have demonstrated effectiveness. In fog computing environments, machine learning techniques make migration decisions based on multiple factors rather than single variables like mobility or load. Experimental evaluations show these approaches can reduce latency for time-critical applications by considering comprehensive system state variables.

Error Correction and Fault Tolerance

Error correction and fault tolerance are essential for maintaining DMLN reliability in challenging network conditions. Traditional approaches often struggle with error detection and correction in rapidly changing decentralized environments. The challenge increases when considering resource limitations that prevent transmission of additional parity packets.

Machine learning-based approaches have shown promise for addressing these challenges. Adaptive error correction systems employ machine learning to dynamically adjust correction parameters based on network conditions, significantly enhancing wireless communication robustness and efficiency. Natural redundancy in data can also be exploited using machine learning for error correction, either with or without prior knowledge of data representation formats.

For blockchain-based systems specifically, consensus algorithms like Byzantine Fault Tolerance provide enhanced reliability by electing authenticated devices to ensure data block integrity. Experimental results show hybrid blockchain scenarios outperform non-hybrid implementations in terms of throughput and latency metrics. These approaches can be further enhanced by incorporating machine learning for predictive fault detection.

Future Directions and Research Opportunities

The future of DMLN presents numerous exciting research opportunities across multiple domains. Emerging technologies like post-quantum blockchain will address potential threats from quantum computing advancements. Enhanced integration between blockchain and machine learning will further improve security, privacy, and efficiency across applications from healthcare to finance.

For technical advancement, research into more efficient consensus mechanisms remains crucial. Sustainable blockchain approaches that balance security requirements with energy efficiency will enable broader adoption of DMLN in resource-constrained environments. Consensus mechanisms must evolve to address the complex tradeoffs between decentralization, security, and scalability.

Standardization efforts will be essential for enabling interoperability between different DMLN implementations. As these systems become more widespread, ethical considerations around privacy, fairness, and accessibility will require careful attention from researchers and practitioners. Development of transparent, explainable models will further enhance trust in these systems.

Conclusion

Decentralized Machine Learning Networks represent a transformative approach to distributed computing that addresses fundamental limitations in traditional machine learning architectures. By integrating blockchain technology with machine learning algorithms across decentralized infrastructures, these systems enable privacy-preserving, secure, and efficient collaborative learning. The architecture's strengths in fault tolerance, data protection, and computational efficiency make it suitable for applications ranging from healthcare data analysis to vehicular networks and financial systems.

While challenges in scalability, resource management, security, and computation overhead persist, innovative solutions continue to emerge. Alternative ledger technologies, efficient consensus mechanisms, multi-layered security approaches, and adaptive resource management strategies demonstrate the field's rapid advancement. Virtual machine implementations and deduplication techniques further enhance the practicality of these systems for production environments.

As research progresses, DMLN have the potential to fundamentally transform how organizations process, analyze, and interact with data. The combination of localized data processing with global model improvements offers a promising path toward more efficient, private, and secure machine learning. Through continued innovation and cross-disciplinary collaboration, DMLN will likely play an increasingly important role in the future computing landscape, enabling new applications while preserving the core values of privacy, security, and decentralization.

Sources:
[1] Decentralized Stochastic First-Order Methods for Large-scale Machine Learning, https://www.semanticscholar.org/paper/3f7b54e93bcae9c48eee9e68a514bf662effa8d8
[2] DECENTRALIZED CONVEX OPTIMIZATION FOR WIRELESS SENSOR NETWORKS, https://www.semanticscholar.org/paper/cc12db4f1385ba4c5ed1041e38357cae4df883f8
[3] Federated Learning; Privacy Preserving Machine Learning for Decentralized Data, https://www.semanticscholar.org/paper/58844d1d12320a8af7ac5fc9c80f285121e514c5
[4] Decentralized Machine Learning Governance: Overview, Opportunities, and Challenges, https://www.semanticscholar.org/paper/b8eaa424808269ded2cc8ea554e1b28de198273e
[5] Synergistic Integration of Blockchain and Machine Learning: A Path to a Decentralized Intelligent Future, https://www.semanticscholar.org/paper/8cb47b2101e18b4c46db39c337980b5b027d08ef
[6] Machine Learning for Bandwidth Management in Decentralized Networks, https://www.semanticscholar.org/paper/287a8ee440b521e36d1d3f35d53ce3eb44d5267b
[7] Machine Learning Applications in Optical Networks, https://www.semanticscholar.org/paper/7444700a5864666b7b29a20a907d35b8d081c22b
[8] A Decentralized Collaborative Learning Approach in 5G+ Core Networks, https://www.semanticscholar.org/paper/b5a6572a015ef07b88151858003529f493f5c7d1
[9] Stochastic Distributed Optimization for Machine Learning from Decentralized Features, https://www.semanticscholar.org/paper/b7a7938d24ed1fbc3cbb24180d2abecc40dcad43
[10] Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks, https://www.semanticscholar.org/paper/2b08ec4b24d178de2c4c461cf08962372765868a
[11] Machine Learning Applications in Wireless Sensor Networks: A Review, https://www.semanticscholar.org/paper/23d93569a50167365f5f0bc48b3da514bc6b15e3
[12] Tangle Ledger for Decentralized Learning, https://www.semanticscholar.org/paper/ae0187893d957d3e731b66c7504869c8340923d1
[13] UAV-Aided Decentralized Learning over Mesh Networks, https://www.semanticscholar.org/paper/6a2a731719b9e3d7a680955ef3b235e8b862da60
[14] Neural networks for machine learning applications, https://www.semanticscholar.org/paper/6029bc48ad656f78993065c34c736e151fe0f2e0
[15] A Decentralized Blockchain-Enabled Federated Learning Approach for Vehicular Networks, https://www.semanticscholar.org/paper/93cc61a4218387e5b47fb5a79482c85379201e24
[16] Decentralized Machine Learning over the Internet, https://www.semanticscholar.org/paper/2f7e9fffd023eeba5f14aa1e36dffeb29a1f62e1
[17] Cluster Based Pseudo Hierarchical Decentralized Federated Learning in UAV Networks, https://www.semanticscholar.org/paper/94f552dc1e3f9bf5100c00a8f53429f4ab1ddffa
[18] Communication-efficient Decentralized Machine Learning over Heterogeneous Networks, https://www.semanticscholar.org/paper/1dcd3ce2221eff4213487249978f7ec844f1c611
[19] The Non-IID Data Quagmire of Decentralized Machine Learning, https://www.semanticscholar.org/paper/206261db1196e4e391ca42077f6fca6b3ece34d0
[20] FedADSN: Anomaly detection for social networks under decentralized federated learning, https://www.semanticscholar.org/paper/f2e15ff22ddc70525e32d970c1ae3e532cc79dbc
[21] Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities, https://www.semanticscholar.org/paper/4cf7ddc154304854f941f9e66cf9dccecf502f82
[22] Machine Learning-Enhanced Decentralized Finance (DeFi), https://www.semanticscholar.org/paper/3cd3437b08ddf3033d4f63c98b45b5ae9618aa45
[23] Machine learning-based decentralized TDMA for VLC IoT networks, https://www.semanticscholar.org/paper/58e02dfa65de35b1edf193adee4ec38c74747819
[24] Enhancing Data Security and Privacy through Blockchain and Machine Learning Integration, https://www.semanticscholar.org/paper/cd6697d21c1b7010069c9fe31d069d1712277dc6
[25] Blockchain Based Trusted Distributed Machine Learning for Credit Scoring, https://www.semanticscholar.org/paper/ae4fb7872044943572eb2ec374013bef29f90dc4
[26] Accelerating and Securing Blockchain-Enabled Distributed Machine Learning, https://www.semanticscholar.org/paper/603fddfead0bd75bcc85b3f2b50e1bcb3f75cef7
[27] Integration of Blockchain and Machine Learning for Microgrids, https://www.semanticscholar.org/paper/eba4de8e43a79180bff68b252c72832dcc1739d7
[28] TDML - A Trustworthy Distributed Machine Learning Framework, https://www.semanticscholar.org/paper/64aae44ac26b3bf40496a14300546a4f86fda5f8
[29] Utilizing Blockchain for Distributed Machine Learning based Intrusion Detection in Internet of Things, https://www.semanticscholar.org/paper/0da2759c4eefa157104a633452cf4be3e2e5d96a
[30] Blockchain and Machine Learning for Automated Compliance in Regulatory Technology, https://www.semanticscholar.org/paper/dfcf349e146584a89f113620b285711b189588da
[31] Integrating Homomorphic Encryption with Blockchain Technology for Machine Learning Applications, https://www.semanticscholar.org/paper/6089768d3be90b1f44ff30d74ac390ffe834e02b
[32] Blockchain Technology Provides Machine Learning, Cloud Computing and Secure Data Transmission, https://www.semanticscholar.org/paper/244c39ec723c85a12c7d0aa7ea139010563331e2
[33] Blockchain Integration with Machine Learning for Securing Fog Computing Vulnerability in Smart City Sustainability, https://www.semanticscholar.org/paper/edea07a00665e3531c0e3a25b41bc4ee135661f0
[34] Blockchain-Enhanced Federated Learning: A New Paradigm for Secure Distributed Machine Learning, https://www.semanticscholar.org/paper/ea1e3168a5af1c703b8a53266a89fd3e5696548b
[35] A Novel Framework for Cloud Data Security with Blockchain Technology and Distributed Virtual Machine Agents, https://www.semanticscholar.org/paper/da30372edf26b33f34394d10c3a91efdf6f04472
[36] Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing, https://www.semanticscholar.org/paper/8193c2c793be27d5aae641346a4d655a62a0e9a9
[37] Advances in Blockchain-Based Internet of Vehicles Application: Prospect for Machine Learning Integration, https://www.semanticscholar.org/paper/c87ff68c68eae6a402a7d6c2b375356bda5fcaf8
[38] Mobile edge fog, Blockchain Networking and Computing-A survey, https://www.semanticscholar.org/paper/00c55d38f347f9f4dc590b4c97fba6e82b047ff6
[39] Integration of Blockchain Technology and Machine Learning in Online Secure Banking System, https://www.semanticscholar.org/paper/8a7dd408d9185fc47d1d90bb6ef0beba58215ba1
[40] Blockchain for Distributed Systems Security in Cloud Computing: A Review of Applications and Challenges, https://www.semanticscholar.org/paper/9f34abd3783522327e000c31fd8d6f47f6058c95
[41] Hardware-assisted Decentralized Resource Management for Networks on Chip with QoS, https://www.semanticscholar.org/paper/87969ba5ea5bf81f522e0c6bfdc9a67f2c55861d
[42] Enhancing Human-computer Interaction through Use of Embedded COGNET Models, https://www.semanticscholar.org/paper/06b0ecb98c20c9c226a8ca0bfff04a79d2725b55
[43] Computer Vision and Human-Computer Interaction: artificial vision techniques and use cases with creating interfaces and interaction models, https://www.semanticscholar.org/paper/ad8fb11bd79c48e1b7e3c58108d0e4017575b345
[44] Decentralized Resource Management for a Distributed Continuous Media Server, https://www.semanticscholar.org/paper/5698f5511d1f655e00307101c457ce4d494d610a
[45] Efficient Forward Error Correction for Reliable Transmission in Packet Networks, https://www.semanticscholar.org/paper/808fb1c8f7013775a4136bcbfe33d4f15306e0ba
[46] Poster: Decentralized Simulation Workflow for Enhancing Connected Vehicle Security, https://www.semanticscholar.org/paper/c8ed653b94dd20986fc1b243eac277c7db59358c
[47] Deep neural networks for multimodal perception and human-computer interaction technology in art design, https://www.semanticscholar.org/paper/863c053f92626667f8d15ed3fc5c4f2e19100c93
[48] Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum, https://www.semanticscholar.org/paper/df07492ea7a12f8a9a1cd60f0f63d0b8275f78fa
[49] A Three-layer Security Assurance Model for a Decentralized Federated Learning System, https://www.semanticscholar.org/paper/453690c02120baf63a32e161d756ce5f94f6024e
[50] Blockchain-Enabled Secure and Decentralized Resource Management for Open Radio Access Network Cellular Networks, https://www.semanticscholar.org/paper/d751a43d38a4420484ea51382ad9b835ca25686c
[51] A decentralized intrusion detection system for increasing security of wireless sensor networks, https://www.semanticscholar.org/paper/363c23625db36597e0ae2f3f5942ecbdef226ec7
[52] Error correction for resource limited random network coding networks, https://www.semanticscholar.org/paper/32bd58c5eda26822948f64a648f75a80f8d638b8
[53] Error correction in heterogeneous wireless sensor networks, https://www.semanticscholar.org/paper/050c0dfa475d3f4ed3266971c2b51d3dcb8a9b6a
[54] Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments, https://www.semanticscholar.org/paper/62ae70bc0e37b1ce45c77b6129705bfbc1e734bf
[55] Scalability Challenges in Blockchain Networks: A Comparative Analysis, https://www.semanticscholar.org/paper/c6b76b2c09c23315213278df0b9b7f5f5f6d464b
[56] Scalability and Robustness of Federated Learning Systems: Challenges and Solutions, https://www.semanticscholar.org/paper/ca84919c3e557c07550399de5afefaaf5441afc8
[57] Federated Reinforcement Learning for Decentralized Voltage Control in Distribution Networks, https://www.semanticscholar.org/paper/7b44e1687ffdb7d95defa92c9c4796c663cac0c7
[58] Scalability of findability: decentralized search and retrieval in large information networks, https://www.semanticscholar.org/paper/df97b0736a162e7dded74453b14347a4c90c6073
[59] Federated Learning: Enhancing Data Privacy and Security in Machine Learning through Decentralized Training Paradigms, https://www.semanticscholar.org/paper/37ffdc0eae143feabab9c8e1926d1fb1e6ec75a8
[60] Decentralized Machine Learning for Dynamic Resource Optimization in Wireless Networks using Reinforcement Learning, https://www.semanticscholar.org/paper/c07e1a09271750dc84b151dbec1c124e4584d569
[61] Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality, https://www.semanticscholar.org/paper/e99d8f11f332e5511826b11e6883160cd7d34551
[62] Nonlinear Decentralized Federated Localization Framework for Dynamic Vehicular Networks, https://www.semanticscholar.org/paper/12f9479b01f67de24e6013f46f57ce46b1f828a5
[63] Integrating Blockchain and Machine Learning with 6G for Autonomous Vehicle Communication: Achieving Secure, Transparent, and Scalable V2X Networks, https://www.semanticscholar.org/paper/373b55a30d8e34bf8627010ef1e3e32002ee6b39
[64] A Novel Approach to Mitigating Packet Loss in Wireless Communication Networks through Machine Learning-Based Adaptive Error Correction, https://www.semanticscholar.org/paper/6783babd013ff0c224d86080f1368d3ecae95740
[65] Machine Learning for Error Correction with Natural Redundancy, https://www.semanticscholar.org/paper/c30fcb8456efa72d25918b3be7408c8a10841807
[66] Adaptive Decentralized Federated Learning in Resource-Constrained IoT Networks, https://www.semanticscholar.org/paper/abd0ee304bf7608cf0723407142d784ab931fdb2
[67] Distributed Ledger Technologies for M2M Communications, https://www.semanticscholar.org/paper/e395449408512ae24a63ad1d9fa66712ab5c83f5
[68] DEVELOPMENT OF DAG BLOCKCHAIN MODEL, https://www.semanticscholar.org/paper/35ff936aa9adf6d96010e57e3122b692dfe12d09
[69] Distributed Ledger Technologies Based Microgrid Energy Management Using IOTA Tangle, https://www.semanticscholar.org/paper/a3d45535a2c1fbd05e299e4ed5a12af72d5b7af8
[70] Performance Comparison of Directed Acyclic Graph-Based Distributed Ledgers and Blockchain Platforms, https://www.mdpi.com/2073-431X/12/12/257
[71] Sustainable Blockchain: A New Horizon for Energy-Efficient Consensus Mechanisms, https://www.semanticscholar.org/paper/28327d116dcf90d304adf896a09e3a376dd3b377
[72] Comparing Core Consensus mechanisms for Education Blockchains, https://www.semanticscholar.org/paper/52f321e3d562c92d78499614525a664a5bcc09de
[73] Comparison on Proof of Work Versus Proof of Stake and Analysis on Why Ethereum Converted to Proof of Stake, https://www.semanticscholar.org/paper/b65dc47ad9f69b2141fa6aad45216d3940c3c131
[74] Proof of Work vs. Proof of Stake in Cryptocurrency, https://www.semanticscholar.org/paper/ae2009f9f9fdb8b46579ac93fdc9e9f40f02c48b
[75] Proof-of-stake at stake: predatory, destructive attack on PoS cryptocurrencies, https://www.semanticscholar.org/paper/8fe6635ad0a4812862824df688e2ab7f65713de0
[76] Decentralized Machine Learning on a Blockchain: Case Studies, https://www.semanticscholar.org/paper/cbdce3d487ff1aa386be5750ecc68980b9365617
[77] Analysis of Fault Tolerance in Permissioned Blockchain Networks, https://www.semanticscholar.org/paper/22955c16ead7fb69e47f1ccf0951659148ecd8b5
[78] Secure, Decentralized, Privacy Preserving Machine Learning System Implementation over Blockchain, https://www.semanticscholar.org/paper/24bd45c4a51c7fbca416c8147a2fa3df9d2f8099
[79] Blockchain-Based Decentralized Federated Learning Model, https://www.semanticscholar.org/paper/13f38345f5e3d67707db3cbb81889303991f8743
[80] Exploring the role of blockchain technology in enhancing data integrity and privacy protection, https://www.semanticscholar.org/paper/bab7ce505c23e78fc30e9db07961f6c801e7fe48
[81] Implementation of Fault-Tolerance Mechanism in Quorum-Based Blockchain Provisioning in Cloud Infrastructure Using Replication and Monitoring Protocols, https://www.semanticscholar.org/paper/5c6702889351c220213ee2b99cd0214bfc296fd2
[82] Fault-Tolerance System Design in the Internet of Things Network with Blockchain Validation, https://www.semanticscholar.org/paper/1cd6f8950b0d864f20f6d86a800b6d180d5c231b
[83] Blockchain Solution for IoT-based Critical Infrastructures: Byzantine Fault Tolerance, https://www.semanticscholar.org/paper/1df371b3d554fb6f8ac5c705dba1d3725c23f992
[84] A Review on Blockchain and its Future Scope, https://www.semanticscholar.org/paper/f477fba6e6812180cdf2aead3cc94dc0e75035ee
[85] Decentralized Framework for Securing Smart Grids Using Blockchain and Machine Learning, https://www.semanticscholar.org/paper/8685875167b2d84f5c7aceccaf11922e0ab99574
[86] IPFS-based Blockchain Enabled System for Secure Data Storage and Access in Healthcare, https://www.semanticscholar.org/paper/3787a0a3c64dc58ee6dac2c85ffd939c3744be7a
[87] A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing, https://www.semanticscholar.org/paper/6940c76ff34168894b126917f0c27abba248023b
[88] Efficient Deduplication in a Distributed Primary Storage Infrastructure, https://www.semanticscholar.org/paper/c12627d74417d11932e2cd29312333ca80c2e1ff
[89] Analytical Study of Virtual Machine Allocation Techniques in Cloud Computing Using Machine Learning, https://www.semanticscholar.org/paper/58982d44a189d73054565be505958c81f57ac76c
[90] Virtual machine management system and use method thereof, https://www.semanticscholar.org/paper/5483e2b2ae1b22bd43828c52c4f60a164e424332
[91] Virtual Machine Storage Performance using SR-IOV by c, https://www.semanticscholar.org/paper/83308ade7fd28e674e39032d9c9b4ac4342fed95
[92] XHive: Efficient Cooperative Caching for Virtual Machines, https://www.semanticscholar.org/paper/b522597f20f69526bb748a2b592d8db8e9983dac
[93] Virtual Control Storage - Security Measures in VM/370, https://www.semanticscholar.org/paper/e158e44c03e3a6ac5a6cb9b384982faf143572f2
[94] Reinforcement Learning-Driven Decision-Making for Live Virtual Machine Migration in Fog Computing, https://www.semanticscholar.org/paper/6fcfa7346606e6b7c78b2b59d1e8b91e29564ac9
[95] Verification of Security Policies in Decentralized Management of Collaboration Systems, https://www.semanticscholar.org/paper/f1d8f126f7d69fb3f873bd4e2a3be4b3826986ec
[96] Joint schemes for physical layer security and error correction, https://www.semanticscholar.org/paper/dd44bb86fe400181ca00cd152448590177183dd8
[97] Blockchain-based Decentralized Identity Management for Healthcare Systems, https://www.semanticscholar.org/paper/7ccdfbfc8d624633175fc86f877309b128907b89
[98] Grouping and Layered Key Management Strategy in WSN Based on EBS, https://www.semanticscholar.org/paper/efcbca29a42395fc2e1137dc7739751463701932
[99] Decentralized Data Privacy Protection and Cloud Auditing Security Management, https://www.semanticscholar.org/paper/1f5ae812a9185d6d5e75b2fd6baf0c9f30761d8b
[100] Blockchain Technology in Energy Management Systems: Enhancing Security and Transparency, https://www.semanticscholar.org/paper/0f29fb75aec95dc91ed3f9ced90e772f6a710e12
[101] A Multi-Layered Security Model for Learning Management System, https://www.semanticscholar.org/paper/d2eb5164ed9130a63c867a8f9a1c93b6c27c046d
[102] Trust Management in Growing Decentralized Networks, https://www.semanticscholar.org/paper/6da381f036dac6573a97f4d0039b71ff4fdbbcd7
[103] Gradient tracking and variance reduction for decentralized optimization and machine learning, https://www.semanticscholar.org/paper/6e47a506a2d2032f930fe087a7cae1fe5ada0d3e
[104] Simple, Efficient and Convenient Decentralized Multi-Task Learning for Neural Networks, https://www.semanticscholar.org/paper/b6e9db5d08df3fc37393b40554f5f6833a7b29fd
[105] Decentralized collaborative machine learning for protecting electricity data, https://www.semanticscholar.org/paper/97522c8ff6fac9797cf2024ab34cca677c46c98e
[106] Federated Learning: Collaborative Machine Learning without
Centralized Training Data, https://www.semanticscholar.org/paper/6a6ad9eb495739f4c80e7c09598720c3d5c5dff7