Federated learning (FL) is currently the most widely adopted framework for collaborative
training of (deep) machine learning models under privacy constraints. Albeit its popularity, it …

Numerous machine learning models can be formulated as a stochastic minimax optimization
problem, such as imbalanced data classification with AUC maximization. Develo** …

Federated Learning is a distributed learning paradigm with two key challenges that
differentiate it from traditional distributed optimization:(1) significant variability in terms of the …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Federated learning (FL) allows model training from local data collected by edge/mobile
devices while preserving data privacy, which has wide applicability to image and vision …

We provide a new analysis of local SGD, removing unnecessary assumptions and
elaborating on the difference between two data regimes: identical and heterogeneous. In …