Scalability and Robustness of Federated Learning Systems: Challenges and Solutions

Abstract

Federated Learning (FL) revolutionizes traditional machine learning paradigms by enabling collaborative model training across decentralized devices while preserving data privacy. However, its scalability and robustness remain formidable challenges. This paper delves into the complexities of scaling FL systems and enhancing their resilience in dynamic environments. We analyze scalability hurdles stemming from communication overhead, resource constraints, and diverse client populations. Additionally, we scrutinize robustness challenges posed by non-IID data distributions, heterogeneity, and adversarial threats. Proposing novel solutions, including communication-efficient aggregation techniques, adaptive client sampling strategies, and robust aggregation mechanisms, we aim to advance the scalability and robustness of FL systems. Empirical evaluations and case studies underscore the efficacy of these solutions across various applications. Our work outlines future research directions, emphasizing standardization efforts and ethical considerations, to propel the adoption of FL in real-world scenarios.. Keywords: Keywords: Federated Learning, Scalability, Robustness, Privacy Preservation, Communication Efficiency, Heterogeneity, Adversarial Attacks, Differential Privacy.