Research on Federated Learning Algorithms Driven by Data Heterogeneity

Federated Learning as a distributed machine learning paradigm enables collaborative modeling among multiple participants while preserving data privacy. However, challenges such as model convergence difficulties and low communication efficiency caused by client-side data heterogeneity remain critical bottlenecks hindering its practical applications. This paper constructs a three-dimensional analytical framework encompassing "client-local optimization, server aggregation strategies, and global convergence guarantees" based on mathematical characterization of data heterogeneity. Through systematic analysis of core research achievements, we reveal evolutionary patterns of key technical approaches including dynamic learning rate adaptation, gradient correction mechanisms, and heterogeneity-aware regularization. The study further identifies three fundamental challenges: multi-objective optimization dilemmas, inadequate adaptability to dynamic data drift, and theory-practice gaps. Future breakthroughs should focus on cross-modal knowledge transfer architectures and trusted federated learning mechanisms to enable reliable algorithm deployment in open environments.