Federated learning (FL) is a machine learning approach that enables the training of an algorithm across multiple decentralised devices or servers while holding local data samples without exchanging them. However, this technique faces significant challenges due to heterogeneity in data distribution, computation capabilities, and communication systems among the participating nodes.

As a result of this, several studies have suggested several steps to address this issue, such as: Cross-Device and Cross-Silo Configurations, Advanced Aggregation Methods, Client Clustering, Personalisation Techniques, Adaptive Federated Optimisation, Incentive Mechanisms, Efficient Communication Strategies, Robustness and Privacy Enhancements, Decentralised and Hierarchical Learning, and Dynamic Participation and Resource Allocation.

It's worth nothing that addressing the challenges of heterogeneity in federated learning using some of the suggested approaches requires the application of different techniques to strategic system design and management.

In federated learning, mitigating the challenges posed by heterogeneity involves a multi-faceted approach. Adaptive federated optimization techniques, such as client weighting and adaptive learning rates, can help balance the contributions across diverse clients. Model personalization, through customization or meta-learning, tailors models to individual clients, enhancing performance. Advanced aggregation algorithms like FedAvg and its variants, alongside robust aggregation methods, aim to integrate updates more effectively. Data augmentation and synthetic data generation improve model generalization, while resource-aware scheduling and selective participation optimize the use of computational resources. Decentralized learning architectures, like hierarchical federated learning, manage heterogeneity within subgroups efficiently. Lastly, incentive mechanisms encourage meaningful participation, and privacy-preserving techniques like differential privacy ensure the protection of sensitive information during the learning process. Together, these strategies form a comprehensive approach to address the complexities introduced by heterogeneity in federated learning environments....