Fractional Order Federated Learning

This paper introduces FOFedAvg, an improved version of federated learning (a technique where multiple remote computers train an AI model together while keeping their data private) that uses fractional-order stochastic gradient descent (a math-based method that remembers past training steps to learn better). FOFedAvg addresses common problems in federated learning like slow training speed, heavy communication costs, and issues when different clients have different types of data by using memory-aware updates that improve both efficiency and stability.