RepuNet: A Reputation System for Mitigating Malicious Clients in DFL

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 in model aggregation is adjusted based on their reputation scores. RepuNet was integrated into the Nebula DFL platform and experimentally evaluated with MNIST and CIFAR-10 datasets under non-IID distributions, using federations of up to 25 nodes in both fully connected and random topologies. Different attack intensities, frequencies, and activation intervals were tested. Results demonstrated that RepuNet effectively detects and mitigates malicious behavior, achieving F1 scores above 95% for MNIST scenarios and approximately 76% for CIFAR-10 cases. These outcomes highlight RepuNet's adaptability, robustness, and practical potential for mitigating threats in decentralized federated learning environments.