Distributed Stochastic

Distributed stochastic optimization tackles the challenge of solving large-scale optimization problems by distributing the computational load across multiple agents. Current research focuses on developing and analyzing algorithms like Federated Averaging and its variants, addressing issues such as asynchronous communication, delayed observations, and heterogeneous data distributions across agents, often employing neural network architectures for complex problems. This field is crucial for advancing machine learning applications, particularly in scenarios with massive datasets or limited communication bandwidth, enabling efficient and scalable solutions for diverse problems ranging from tomography reconstruction to multi-agent reinforcement learning.