RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
Isaac Marroqui Penalva, Enrique Tom\'as Mart\'inez Beltr\'an, Manuel Gil P\'erez, Alberto Huertas Celdr\'an

TL;DR
RepuNet is a decentralized reputation system designed to identify and mitigate malicious nodes in DFL, improving robustness without relying on heavy infrastructure like blockchain.
Contribution
It introduces a dynamic reputation mechanism based on multiple behavioral metrics to enhance security in decentralized federated learning.
Findings
RepuNet achieves over 95% F1 score on MNIST.
RepuNet maintains about 76% F1 score on CIFAR-10.
The system effectively detects various malicious behaviors.
Abstract
Decentralized Federated Learning (DFL) enables nodes to collaboratively train models without a central server, introducing new vulnerabilities since each node independently selects peers for model aggregation. Malicious nodes may exploit this autonomy by sending corrupted models (model poisoning), delaying model submissions (delay attack), or flooding the network with excessive messages, negatively affecting system performance. Existing solutions often depend on rigid configurations or additional infrastructures such as blockchain, leading to computational overhead, scalability issues, or limited adaptability. To overcome these limitations, this paper proposes RepuNet, a decentralized reputation system that categorizes threats in DFL and dynamically evaluates node behavior using metrics like model similarity, parameter changes, message latency, and communication volume. Nodes' influence…
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