Fairness-guided federated training for generalization and personalization in cross-silo federated learning

• Addresses the dual challenge of generalization and personalization in cross-silo federated learning (GPFL) under domain shift. • Proposes FFT-GP, which integrates fairness-aware aggregation (FAA) to minimize generalization gap variance among clients. • Employs a meta-learning strategy to align local training with the global model’s feature distribution, balancing generalization and personalization. • Demonstrates superior efficacy over existing methods, enhancing FL systems in practical cross-silo scenarios.