A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus

Abstract

Federated learning has been widely studied and applied to various scenarios. In mobile computing scenarios, federated learning protects users from exposing their private data, while cooperatively training the global model for a variety of real-world applications. However, the security of federated learning is increasingly being questioned, due to the malicious clients or central servers' constant attack to the global model or user privacy data. To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i.e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC). The framework uses blockchain for the global model storage and the local model update exchange. To enable the proposed BFLC, we also devised an innovative committee consensus mechanism, which can effectively reduce the amount of consensus computing and reduce malicious attacks. We then discussed the scalability of BFLC, including theoretical security, storage optimization, and incentives. Finally, we performed experiments using real-world datasets to verify the effectiveness of the BFLC framework.

arXiv comment7 pages, 4 figures and 1 table

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Cited by in corpus (35)

• Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing
• Decentralized Federated Learning: A Survey on Security and Privacy
• A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee
• Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
• A Platform-Free Proof of Federated Learning Consensus Mechanism for Sustainable Blockchains
• Privacy-Preserving in Blockchain-based Federated Learning Systems
• An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation
• A Systematic Survey of Blockchained Federated Learning
• Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
• Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations
• ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learning
• Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus
• Fairness and Accuracy in Federated Learning
• A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
• GFL: A Decentralized Federated Learning Framework Based On Blockchain
• Privacy-Preserving Healthcare Data in IoT: A Synergistic Approach with Deep Learning and Blockchain
• DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology
• Fusion of Machine Learning and Blockchain-based Privacy-Preserving Approach for Health Care Data in the Internet of Things
• Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning
• DFL: High-Performance Blockchain-Based Federated Learning
• LiteChain: A Lightweight Blockchain for Verifiable and Scalable Federated Learning in Massive Edge Networks
• Homogeneous Learning: Self-Attention Decentralized Deep Learning
• FedGL: Federated Graph Learning Framework with Global Self-Supervision
• Collaborative Distributed Machine Learning
• Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework
• DeRelayL: Sustainable Decentralized Relay Learning
• Proof of Swarm Based Ensemble Learning for Federated Learning Applications
• Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent
• Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator
• MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning
• Reward-Based 1-bit Compressed Federated Distillation on Blockchain
• Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach
• FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systems
• FLoW3 -- Web3 Empowered Federated Learning
• Trends in Blockchain and Federated Learning for Data Sharing in Distributed Platforms