Communication-Efficient Learning of Deep Networks from Decentralized Data, H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Aguera y Arcas, Felix Hanzely, 2017Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Vol. 54 (Proceedings of Machine Learning Research)DOI: 10.55982/aistats.2017.96 - Introduces the Federated Averaging algorithm, which serves as the baseline whose limitations are discussed.
Federated Optimization in Heterogeneous Networks, Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith, 2020Proceedings of Machine Learning Research, Vol. 119 - Analyzes the impact of statistical heterogeneity (Non-IID data) and client drift on FedAvg's convergence and performance.