Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

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

In federated learning, heterogeneity in the clients' local datasets and computation speeds
results in large variations in the number of local updates performed by each client in each …

Investigation of the degree of personalization in federated learning algorithms has shown
that only maximizing the performance of the global model will confine the capacity of the …

In federated learning, communication cost is often a critical bottleneck to scale up distributed
optimization algorithms to collaboratively learn a model from millions of devices with …

We study local SGD (also known as parallel SGD and federated SGD), a natural and
frequently used distributed optimization method. Its theoretical foundations are currently …

Federated Learning (FL) is popular for communication-efficient learning from distributed
data. To utilize data at different clients without moving them to the cloud, algorithms such as …

In federated distributed learning, the goal is to optimize a global training objective defined
over distributed devices, where the data shard at each device is sampled from a possibly …

When training machine learning models using stochastic gradient descent (SGD) with a
large number of nodes or massive edge devices, the communication cost of synchronizing …

In this paper, we study communication efficient distributed algorithms for distributionally
robust federated learning via periodic averaging with adaptive sampling. In contrast to …