Vol. 4, Issue 9, Part D (2018)

The challenges in Federated Learning are multifaceted, encompassing issues related to communication efficiency, model aggregation, and heterogeneity of data distributions. We critically analyze the impact of communication overhead on FL systems and propose strategies for mitigating these challenges, emphasizing the importance of optimizing communication protocols to enhance overall efficiency. Additionally, the paper addresses the intricacies of model aggregation techniques in federated settings, shedding light on the trade-offs between centralized and decentralized approaches. A comprehensive exploration of the implications of data heterogeneity in FL settings is also presented, emphasizing the need for robust algorithms capable of accommodating diverse and dynamic datasets.

In parallel, this review identifies key opportunities that arise from the unique characteristics of Federated Learning. The inherent privacy preservation in FL models is a notable advantage, and we discuss methods to further enhance privacy mechanisms while maintaining model performance. Furthermore, we explore the potential for collaborative learning in federated settings, emphasizing the synergy that can be achieved by leveraging the collective intelligence of edge devices. The paper also discusses the role of Federated Learning in edge computing, where decentralized model training can harness the computational power of edge devices for real-time and resource-efficient learning.