Slow "straggler" clients normally stall training rounds. This framework applies split learning to stragglers so the server carries part of their compute — keeping aggregation on time and conserving client resources instead of dropping slow participants.
Straggler-Resilient Federated Learning
A framework that keeps federated learning on schedule even when some clients are slow.