Heterogeneity Challenges of Federated Learning for Future Wireless Communication Networks

Two technologies of great interest in recent years—Artificial Intelligence (AI) and massive wireless communication networks—have found a significant point of convergence through Federated Learning (FL). Federated Learning is a Machine Learning (ML) technique that enables multiple participants to collaboratively train a model while keeping their data local. Several studies indicate that while improving performance metrics—such as accuracy, loss reduction, or computation time—is a primary goal, achieving this in real-world scenarios remains challenging. This difficulty arises due to various heterogeneity characteristics inherent to the wireless devices participating in the Federation. Heterogeneity in Federated Learning arises when participants contribute differently, leading to challenges in the model training process. Heterogeneity in Federated Learning may appear in architecture, statistics, and behavior. System heterogeneity arises from differences in device capabilities, including processing power, transmission speeds, availability, energy constraints, and network limitations, among others. Statistical heterogeneity occurs when participants contribute non-independent and non-identically distributed (non-IID) data. This situation can harm the global model instead of improving it, especially when the data are of poor quality or too scarce. The third type, behavioral heterogeneity, refers to cases where participants are unwilling to engage or expect rewards despite minimal effort. Given the growing research in this area, we present a summary of heterogeneity characteristics in Federated Learning to provide a broader perspective on this emerging technology. We also outline key challenges, opportunities, and future directions for Federated Learning. Finally, we conduct a simulation using the LEAF framework to illustrate the impact of heterogeneity in Federated Learning.