Aggregation algorithms

Hello everyone, I am currently working on the experimental section of my paper. Since this is my first time using federated learning in my tasks, I need to conduct an experiment by trying different federated learning aggregation algorithms to observe the variations in results. Could you please suggest some classic aggregation algorithms that I should consider trying out?



The most basic aggregation algorithm would be FedAvg: flwr.server.strategy.FedAvg (paper).

And then you could look into strategies that just replace the average function of FedAvg, like FedMedian: flwr.server.strategy.FedMedian (paper) or FedTrimmedAvg: flwr.server.strategy.FedTrimmedAvg (paper).

Next, you should have a look at FedAvgM, which adds momentum and learning rate to the aggregation: flwr.server.strategy.FedAvgM (paper), and other adaptive optimization strategies like FedAdam (flwr.server.strategy.FedAdam), FedAdagrad (flwr.server.strategy.FedAdagrad), or FedYogi (flwr.server.strategy.FedYogi), here is the paper proposing those strategies.

Hope this helps!


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