A Blockchain-Based Federated-Learning Framework for Defense against Backdoor Attacks

Abstract

Federated learning (FL) is a technique that involves multiple participants who update their local models with private data and aggregate these models using a central server. Unfortunately, central servers are prone to single-point failures during the aggregation process, which leads to data leakage and other problems. Although many studies have shown that a blockchain can solve the single-point failure of servers, blockchains cannot identify or mitigate the effect of backdoor attacks. Therefore, this paper proposes a blockchain-based FL framework for defense against backdoor attacks. The framework utilizes blockchains to record transactions in an immutable distributed ledger network and enables decentralized FL. Furthermore, by incorporating the reverse layer-wise relevance (RLR) aggregation strategy into the participant’s aggregation algorithm and adding gradient noise to limit the effectiveness of backdoor attacks, the accuracy of backdoor attacks is substantially reduced. Furthermore, we designed a new proof-of-stake mechanism that considers the historical stakes of participants and the accuracy for selecting the miners of the local model, thereby reducing the stake rewards of malicious participants and motivating them to upload honest model parameters. Our simulation results confirm that, for 10% of malicious participants, the success rate of backdoor injection is reduced by nearly 90% compared to Vanilla FL, and the stake income of malicious devices is the lowest.

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“…FL facilitates the creation of a collective learning model via multiple nodes without the need to exchange their data samples [7], thus saving the cost of communication and storage of data to central servers while preserving the privacy of edge data [8,9]. This technique has successfully found applications in diverse domains, including mobile traffic prediction and monitoring [10,11], healthcare [12,13], the internet of things [14][15][16], transportation and autonomous vehicles [17], digital twin [16], blockchain [18], disaster management [19,20], natural language processing [21], knowledge extraction [22], agriculture [23], pharmaceutics, and medical sciences [24,25].…”

“…Actuator Netw. 2024, 13, x FOR PEER REVIEW 2 of 20 things [14][15][16], transportation and autonomous vehicles [17], digital twin [16], blockchain [18], disaster management [19,20], natural language processing [21], knowledge extraction [22], agriculture [23], pharmaceutics, and medical sciences [24,25]. The approach of FL differs from that of distributed machine learning (DML), where the data are initially centralized on a server and subsequently partitioned into subsets for the purpose of learning tasks.…”