Type-Based Unsourced Federated Learning With Client Self-Selection

2026-02-06

AI Summary

This work addresses the challenge of client selection in wireless federated learning under data heterogeneity and stringent privacy constraints by proposing a fully passive self-selection mechanism. Clients autonomously decide whether to participate based solely on their local training loss and a threshold broadcast by the server, without revealing their identities or channel state information. The approach integrates type-based grant-free multiple access with distributed MIMO (D-MIMO) communication, thereby enhancing communication efficiency while preserving privacy. Simulation results demonstrate that the proposed method achieves performance comparable to state-of-the-art server-side selection schemes and significantly outperforms random selection strategies.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Privacy-enhancing technologies

Abstract

We address the client-selection problem in federated learning over wireless networks under data heterogeneity. Existing client-selection methods often rely on server-side knowledge of client-specific information, thus compromising privacy. To overcome this issue, we propose a client self-selection strategy based solely on the comparison between locally computed training losses and a centrally updated selection threshold. Furthermore, to support robust aggregation of clients'updates over wireless channels, we integrate this client self-selection strategy into the recently proposed type-based unsourced multiple-access framework over distributed multiple-input multiple-output (D-MIMO) networks. The resulting scheme is completely unsourced: the server does not need to know the identity of the clients. Moreover, no channel state information is required, neither at the clients nor at the server side. Simulation results conducted over a D-MIMO wireless network show that the proposed self-selection strategy matches the performance of a comparable state-of-the-art server-side selection method and consistently outperforms random client selection.

Problem

• federated learning
• client selection
• data heterogeneity
• privacy
• wireless networks

Innovation

• client self-selection
• unsourced federated learning
• type-based multiple access
• D-MIMO
• channel state information-free