Network Adaptive Federated Learning: Congestion and Lossy Compression
This addresses network efficiency issues in FL for distributed machine learning applications, but it is incremental as it builds on existing compression techniques.
The paper tackles the problem of network congestion in Federated Learning (FL) systems by proposing a Network Adaptive Compression (NAC-FL) policy that dynamically adjusts lossy compression based on congestion, reducing wall clock training time and proving asymptotic optimality under certain assumptions.
In order to achieve the dual goals of privacy and learning across distributed data, Federated Learning (FL) systems rely on frequent exchanges of large files (model updates) between a set of clients and the server. As such FL systems are exposed to, or indeed the cause of, congestion across a wide set of network resources. Lossy compression can be used to reduce the size of exchanged files and associated delays, at the cost of adding noise to model updates. By judiciously adapting clients' compression to varying network congestion, an FL application can reduce wall clock training time. To that end, we propose a Network Adaptive Compression (NAC-FL) policy, which dynamically varies the client's lossy compression choices to network congestion variations. We prove, under appropriate assumptions, that NAC-FL is asymptotically optimal in terms of directly minimizing the expected wall clock training time. Further, we show via simulation that NAC-FL achieves robust performance improvements with higher gains in settings with positively correlated delays across time.