Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?

ArXi:2604.19399v1 Announce Type: new Federated learning (FL) is a key paradigm for distributed model learning across decentralized data sources. Communication in each FL round typically consists of two phases: (i) distributing the global model from a server to clients, and (ii) collecting updated local models from clients to the server for aggregation. This paper focuses on a type of FL where communication between a client and the server is relay-based over dynamic networks, making routing optimization essential.