Exploiting Parasitic Dependency for Free-Rider Elimination in Blockchain-based Federated Learning

Blockchain-based Federated Learning (BFL) facilitates collaborative model training with guaranteed transparency and accountability across distributed participants. However, BFL encounters a critical security vulnerability: advanced free-riders can exploit the public on-chain model histories to synthesize fabricated gradients without performing actual local training, which is indistinguishable from legitimate contributions. Previous efforts to defense free-rider attack mostly focus on anomaly detection, but they struggle to detect some advanced free-riders. To address this fundamental challenge, we propose BFLGR (Blockchain-based Federated Learning with Gresham’s Reversal), which is a novel framework integrating reverse auction mechanisms and a dynamic reputation system. BFLGR employs a dynamic selection algorithm that can eliminate advanced free-riders by exploiting their profit-seeking motives and parasitic dependency on honest participants’ contributions. This framework ensures genuine contributions prevail over deceptive submissions, and achieves a paradigmatic shift in BFL security, transforming the transparency-exploitation vulnerability into a strategic advantage for detecting and eliminating malicious participants. Theoretical proofs and extensive experiments show that BFLGR performs well in advanced free-rider attack situation and outperforms our baselines.

Recommended citation: Donghang Duan, Xu Zheng, Yifu Zheng, Chong Mu, Ruozhou Wang, Ke Yan. (2025). "Exploiting Parasitic Dependency for Free-Rider Elimination in Blockchain-based Federated Learning." 2025 IEEE IPCCC.
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