AI RESEARCH
Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention
arXiv CS.LG
•
ArXi:2605.11752v1 Announce Type: new Federated learning relies on effective client selection to alleviate the performance degradation caused by data heterogeneity. Most existing methods assume full visibility of all clients at each communication round. However, in large-scale or edge-based deployments, the server can only access a subset of clients due to communication, mobility, or availability constraints, resulting in partial visibility where only a subset of clients is observable for aggregation in each communication round.