Asynchronous Federated Learning is a federated learning approach where the server updates the global model immediately upon receiving any client's update — without waiting for all selected clients to finish, eliminating the synchronization barrier that slows down FL with heterogeneous clients.

Asynchronous FL Approaches

• FedAsync: Server applies each client update immediately with a mixing coefficient.
• Staleness Weighting: Weight client updates by their staleness ($alpha^{t - t_k}$) — old updates get less weight.
• Buffered: Wait for a buffer of $K$ updates before aggregating — semi-synchronous middle ground.
• Federated Buffer: Collect updates in a buffer and aggregate when buffer is full.

Why It Matters

• No Stragglers: Synchronous FL waits for the slowest client — async FL is not bottlenecked by stragglers.
• Throughput: Higher model update frequency — more updates per unit time.
• Challenge: Stale updates can degrade convergence — staleness mitigation is essential.

Async FL is don't wait, update now — processing client updates as they arrive for continuous, straggler-free model improvement.