Accelerating and Securing Federated Learning with Stateless In-Network Aggregation at the Edge

• Main Authors: Xia, Junxu, Wu, Wenfei, Luo, Lailong, Cheng, Geyao, Guo, Deke, Nian, Qifeng
• Format: Conference Proceeding
• Language: English
• Subjects: Aggregates; Data privacy; Federated learning; Performance gain; Solid modeling; Throughput; Training
• ISSN: 2575-8411
• DOI: 10.1109/ICDCS60910.2024.00070

In federated learning, sending the trained models (instead of raw data) from clients to the central server can surely decrease the volume of exchanged data and preserve data privacy to some extent. However, the central server can still be a system bottleneck due to the simultaneous and constant model gathering from massive distributed clients. Besides, the central server conducts stateful aggregation (retaining all the updates from each client), making it a potential threat to privacy, since it may recover the raw data based on such model updates inversely. The state-of-the-art methodologies, however, fail to address these two problems concurrently. To this end, we propose GAIN, a secure aggregation acceleration service for federated learning. At its core, GAIN aggregates the model updates at the programmable ingress switches in a stateless manner (storing the aggregated model parameters from the clients temporarily rather than permanently) before proceeding to the central server. Consequently, GAIN can accelerate the transmission and aggregation of model parameters while eliminating the chance of data recovery. We implemented a prototype of GAIN on an FPGA-based testbed to validate its performance. The results demonstrate that GAIN can achieve up to 4.11x speedup in training throughput and reduce up to 86.5% of traffic overhead. Furthermore, through theoretical analysis, we illustrate that GAIN can achieve even more substantial performance gains with a larger number of clients while guaranteeing privacy protection.