Byzantine Resilient Federated Multi-Task Representation Learning

• URL: http://arxiv.org/abs/2503.19209v2
• Date: Tue, 15 Apr 2025 23:53:58 GMT
• Title: Byzantine Resilient Federated Multi-Task Representation Learning
• Authors: Tuan Le, Shana Moothedath,
• Abstract summary: We propose BR-MTRL, a Byzantine-resilient multi-task representation learning framework that handles faulty or malicious agents.<n>Our approach leverages representation learning through a shared neural network model, where all clients share fixed layers, except for a client-specific final layer.
• Score: 1.6114012813668932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose BR-MTRL, a Byzantine-resilient multi-task representation learning framework that handles faulty or malicious agents. Our approach leverages representation learning through a shared neural network model, where all clients share fixed layers, except for a client-specific final layer. This structure captures shared features among clients while enabling individual adaptation, making it a promising approach for leveraging client data and computational power in heterogeneous federated settings to learn personalized models. To learn the model, we employ an alternating gradient descent strategy: each client optimizes its local model, updates its final layer, and sends estimates of the shared representation to a central server for aggregation. To defend against Byzantine agents, we employ two robust aggregation methods for client-server communication, Geometric Median and Krum. Our method enables personalized learning while maintaining resilience in distributed settings. We implemented the proposed algorithm in a federated testbed built using Amazon Web Services (AWS) platform and compared its performance with various benchmark algorithms and their variations. Through experiments using real-world datasets, including CIFAR-10 and FEMNIST, we demonstrated the effectiveness and robustness of our approach and its transferability to new unseen clients with limited data, even in the presence of Byzantine adversaries.

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