FL Algorithm

Federated learning (FL) is a distributed machine learning approach enabling collaborative model training across multiple devices while preserving data privacy. Current research emphasizes improving FL's efficiency and robustness by addressing challenges like limited resources, non-independent and identically distributed (non-IID) data, and adversarial attacks, often employing techniques such as adaptive algorithms, momentum-based methods, and clustered learning strategies. These advancements are crucial for deploying FL in resource-constrained environments like edge computing and IoT networks, impacting various applications from personalized medicine to autonomous vehicles.