This page describes the strategies (Federated Optimization & Aggregation Algorithms) which are avalaible in this framework.

1. FedAvg: Federated Averaging (FedAvg) [McMahan et al., 2016] [Paper].
2. FedProx: Federated Optimization in Heterogeneous Networks [Paper].
3. FedAvgM : Federated Averaging with Momentum [Hsu et al., 2019] [Paper].
4. FedOpt : Adaptive Federated Optimization [Reddi et al., 2020] [Paper].
5. FedAdam : Adaptive Federated Optimization using Adam [Reddi et al., 2020] [Paper].
6. FedMedian : Federated Median [Yin et al., 2018] [Paper].
7. FedLaw : Revisiting Weighted Aggregation in Federated Learning with Neural Networks (ICML-2023) [Paper].
8. PowD : Power Of Choice Client Selection Algorithm. Towards Understanding Biased Client Selection in Federated Learning (AISTATS-2022) [Paper].
9. Scaffold : SCAFFOLD: Stochastic Controlled Averaging for Federated Learning (ICML-2020) [Paper].
10. FedNova : Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization (NeurIPS-2020) [Paper].