Federated learning is a powerful paradigm for large-scale machine learning, but it faces
significant challenges due to unreliable network connections, slow communication, and …

Distributed optimization methods with local updates have recently attracted a lot of attention
due to their potential to reduce the communication cost of distributed methods. In these …

Both centralized and decentralized approaches have shown excellent performance and
great application value in federated learning (FL). However, current studies do not provide …

Gradient tracking methods have emerged as one of the most popular approaches for solving
decentralized optimization problems over networks. In this setting, each node in the network …

Distributed optimization methods with random communication skips are gaining increasing
attention due to their proven benefits in accelerating communication complexity …

Stochastic distributed optimization methods that solve an optimization problem over a multi-
agent network have played an important role in a variety of large-scale signal processing …

The stochastic Gradient Tracking (GT) method for distributed optimization, is known to be
robust against the inter-client variance caused by data heterogeneity. However, the …

We propose a flexible gradient tracking approach with adjustable computation and
communication steps for solving distributed stochastic optimization problems over networks …

As distributed learning applications such as Federated Learning, the Internet of Things (IoT),
and Edge Computing grow, it is critical to address the shortcomings of such technologies …

Decentralized Federated Learning (DFL) has received significant recent research attention,
capturing settings where both model updates and model aggregations--the two key FL …