Domain-Specific Transport Protocols for In-Network Processing at the Edge: A Case Study of Accelerating Model Synchronization
Shouxi Luo, Peidong Zhang, Xin Gang Song, Pingzhi Fan, Huanlai Xing, Long Luo, Hongfang Yu · IEEE Transactions on Mobile Computing · 2025
Nowadays, cross-device federated learning (FL) is the key to achieving personalization services for mobile users and has been widely employed by companies like Google, Microsoft, and Alibaba in production. With the explosive growth in the number of participants, the central FL server, which acts as the manager and aggregator of cross-device model training, would get overloaded, becoming the system bottlenecks. Inspired by the emerging wave of edge computing, an interesting question arises:Could edge clouds help cross-device FL systems overcome the bottleneck?This article provides a cautiously optimistic answer by proposingINP, a FL-specific In-Network Processing framework to achieve the goal. As in-network processing has broken the end-to-end principle of the involved communication and lacks the support of transport protocols, the key is to design domain-specific transport protocols forINP. To fill the gap, we propose the novel Model Download Protocol ofmdpand Model Upload Protocol ofmup. Withmdpandmup, edge cloud nodes along the paths inINPcan easily eliminate duplicated model downloads and pre-aggregate associated gradient uploads for the central FL server, thus alleviating its bottleneck effect, and further accelerating the entire training progress significantly.