High Probability Guarantees for Federated Learning

Federated learning (FL) has emerged as a promising approach for training machine learning models on distributed data while ensuring privacy preservation and data locality. However, one key challenge in FL optimization is the lack of high probability guarantees, which can undermine the trustworthiness of FL solutions. To address this critical issue, we introduce Federated Averaging with post-optimization (FedAvg-PO) method, a modification to the Federated Averaging (FedAvg) algorithm. The proposed algorithm applies a post-optimization phase to evaluate a short list of solutions generated by several independent runs of the FedAvg method. These modifications allow to significantly improve the large-deviation properties of FedAvg which improve the reliability and robustness of the optimization process. The novel complexity analysis shows that FedAvg-PO can compute accurate and statistically guaranteed solutions in the federated learning context. Our result further relaxes the restrictive assumptions in FL theory by developing new technical tools which may be of independent interest. The insights provided by the computational requirements analysis contribute to the understanding of the scalability and efficiency of the algorithm, guiding its practical implementation.