Implementation of asynchronous federated learning in flower currently being developed as a part of my master's thesis.

Currently offers the following modes of operation:

• Unweighted - Upon each merge into the global model, the local updates and global model are weighted equally.
• FedAsync - global_new = (1-alpha) * global_old + alpha * local_new
  • Where alpha depends on the staleness and/or number of samples used
• AsyncFedED - global_new = global_old + $\eta$ * (local_new - global_old)
  • $\eta$ again depends on staleness and/or number of samples

The implementation consists of a modified server, strategy and client manager.

The inspiration of this work stems from the following papers:

• Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing -
• Asynchronous Federated Optimization ~ FedAsync
• AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation
• Federated Learning on Non-IID Data Silos: An Experimental Study

Use the server as typical flower server with one additional argument:

• async_strategy - currently only responsible for aggregating the models.

Moreover the async client manager should be used instead of the SimpleClientManager.

The server instantiation would hence look something like this:

server = AsyncServer(
    strategy=FedAvg(<customized>), 
    client_manager=AsyncClientManager(), 
    async_strategy=AsynchronousStrategy(<customized>))