Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

2021/01/18 by Jun Li, Yumeng Shao, Li, Jun +14 · 6 citations
Computer Science · Social Sciences · #Blockchain Technology Applications and Security #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data #and Cluster Computing (cs.DC) #cs.DC #cs.LG

paper · pdf · doi:10.48550/arxiv.2101.06905

openalex publication_date 2021/01/18 · arxiv created 2021/05/25 · arxiv updated 2021/08/31 · openalex created_date 2022/09/11 · openalex updated_date 2026/07/28

Abstract

Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions, untrustworthy server, and external attacks. To address this issue, we propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL). In a round of the proposed BLADE-FL, each client broadcasts the trained model to other clients, aggregates its own model with received ones, and then competes to generate a block before its local training of the next round. We evaluate the learning performance of BLADE-FL, and develop an upper bound on the global loss function. Then we verify that this bound is convex with respect to the number of overall aggregation rounds K, and optimize the computing resource allocation for minimizing the upper bound. We also note that there is a critical problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to disguise their cheating behaviors. Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients. Based on MNIST and Fashion-MNIST datasets, we show that the experimental results are consistent with the analytical ones. To be specific, the gap between the developed upper bound and experimental results is lower than 5%, and the optimized K based on the upper bound can effectively minimize the loss function.

Cited by

• D2M: A Decentralized, Privacy-Preserving, Incentive-Compatible Data Marketplace for Collaborative Learning
• Trustless Federated Learning at Edge-Scale: A Compositional Architecture for Decentralized, Verifiable, and Incentive-Aligned Coordination
• Analysis and Optimization of Wireless Multimodal Federated Learning on Modal Heterogeneity
• FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
• Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis
• DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions

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