View source for FedAvg

'''FedAvg''' (Federated Averaging) is the dominant aggregation algorithm for [[Federated Learning]], introduced by McMahan et al. in 2017. Each communication round, a subset of clients trains locally on their own data for several steps, then transmits updated model weights to a central server that averages the weights — weighted by each client's dataset size — to produce a new global model. The algorithm's central property is communication efficiency: it reduces the number of rounds needed to train a convergent model compared to naive distributed stochastic gradient descent by performing multiple local gradient steps before each aggregation. Its central limitation is convergence in the non-iid setting: when clients have heterogeneous data distributions (which is always the case in practice), the local updates diverge from the global optimum in a phenomenon called ''client drift'', and the averaged global model may converge to a solution that is suboptimal for most clients. FedAvg assumes that more local computation is always beneficial, but this assumption fails when client data distributions are sufficiently different — a regime that defines most real-world [[Federated Learning]] deployments. Subsequent algorithms — FedProx, SCAFFOLD, MOON — address client drift at additional communication cost, underlining that FedAvg's efficiency gains rest on assumptions that rarely hold. The [[Gradient Descent|optimization landscape]] of FedAvg for deep networks remains an active open problem.

[[Category:Machine Learning]]
[[Category:Distributed Systems]]