The field of decentralized learning and graph-based methods is experiencing significant growth, with a focus on developing novel architectures and algorithms that can efficiently handle complex data and networks. Researchers are exploring new approaches to decentralize learning, such as using random walks and graph theory to improve communication efficiency and scalability. Additionally, there is a growing interest in applying these methods to real-world problems, including political districting and graph partitioning. Notable papers in this area include:

• A paper that introduces a unified framework for jointly optimizing network topology and graph sampling in decentralized federated graph learning, resulting in significant reductions in communication cost and improvements in training performance.
• A paper that presents a new Markov Chain for sampling measures of graph partitions with roughly equal size, which has been shown to efficiently sample target distributions that have been difficult for existing sampling Markov chains.