Model Exchange

Model exchange, the process of sharing or transferring model information between distributed computing entities, is a crucial aspect of federated learning and other distributed machine learning paradigms. Current research focuses on optimizing model exchange strategies for efficiency and privacy, exploring methods like differentially private model fusion and indirect communication via mobile intermediaries (e.g., UAVs) to address challenges in dynamic networks and resource-constrained environments. These advancements are significant for improving the scalability, security, and applicability of federated learning across diverse domains, including healthcare and resource-limited settings. The development of efficient and privacy-preserving model exchange mechanisms is vital for unlocking the full potential of distributed machine learning.