Rethinking Federated Learning Over the Air: The Blessing of Scaling Up

2025-08-25

AI Summary

Federated learning (FL) faces dual challenges: excessive communication overhead and channel impairments (e.g., fading, noise), hindering scalable client coordination. This paper investigates over-the-air federated learning (AirFL), leveraging analog aggregation—where clients transmit analog gradient signals simultaneously over wireless channels for direct in-air computation—to drastically increase the number of concurrently supported clients per round and alleviate communication bottlenecks. Theoretical analysis and experiments demonstrate that scaling up the number of participating clients inherently mitigates small-scale fading, strengthens differential privacy guarantees, and accelerates convergence. An information-theoretic model uncovers fundamental trade-offs among scale, robustness, privacy, and convergence speed. To the best of our knowledge, this work is the first to systematically establish the synergistic benefits of massive client access on AirFL’s robustness, privacy, and efficiency, thereby establishing a new paradigm for high-concurrency, low-latency private computation.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Multiagent Systems: Agent Communication
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Federated learning facilitates collaborative model training across multiple clients while preserving data privacy. However, its performance is often constrained by limited communication resources, particularly in systems supporting a large number of clients. To address this challenge, integrating over-the-air computations into the training process has emerged as a promising solution to alleviate communication bottlenecks. The system significantly increases the number of clients it can support in each communication round by transmitting intermediate parameters via analog signals rather than digital ones. This improvement, however, comes at the cost of channel-induced distortions, such as fading and noise, which affect the aggregated global parameters. To elucidate these effects, this paper develops a theoretical framework to analyze the performance of over-the-air federated learning in large-scale client scenarios. Our analysis reveals three key advantages of scaling up the number of participating clients: (1) Enhanced Privacy: The mutual information between a client's local gradient and the server's aggregated gradient diminishes, effectively reducing privacy leakage. (2) Mitigation of Channel Fading: The channel hardening effect eliminates the impact of small-scale fading in the noisy global gradient. (3) Improved Convergence: Reduced thermal noise and gradient estimation errors benefit the convergence rate. These findings solidify over-the-air model training as a viable approach for federated learning in networks with a large number of clients. The theoretical insights are further substantiated through extensive experimental evaluations.

Problem

• Analyzing performance of over-the-air federated learning with large-scale clients
• Addressing channel-induced distortions in analog parameter transmission
• Investigating scaling effects on privacy, fading mitigation, and convergence

Innovation

• Over-the-air analog transmission for communication efficiency
• Theoretical framework analyzing large-scale client performance
• Channel hardening and noise reduction for convergence