Federated Learning Approaches for Privacy-Preserving Big Data Analytics

Abstract

The blistering growth of big data analytics in industries has transformed how decision-making is done and increased the risk to privacy of centralized machine learning, in which aggregation of sensitive raw data can put information at risk of breaches and inference attacks. The Federated Learning (FL) system provides a decentralized framework that allows performing collaborative model training, but retains data centred on the client devices or institutional servers, thus fulfilling the stringent regulatory standard of regulations like GDPR and HIPAA. The current review is a synthesis of 127 recent papers (20232025) that assess five main privacy-sensible FL methods, namely Standard Federated Averaging (FedAvg), Differential Privacy-enhanced FL (DP-FL, Secure Aggregation, Homomorphic Encryption-based FL (HE-FL) and Hybrid FL frameworks. One of them, DP-FL, is the most widely used method (about 40% of deployments) and it offers a good privacy-utility trade-off with common accuracy degradations of 1-5%. Hybrid designs, particularly those combining differential privacy and secure aggregation, provide defense-in-depth protection, little performance loss (1-4%), and most rapidly increasing deployment rates (34%/year), especially in regulated markets. FL has a high level of practical impact in fundamental areas: healthcare (35% of the applications, e.g., multi-institutional medical imaging and disease prediction), finance (28%, e.g., fraud detection and risk assessment), IoT/smart cities (20%, e.g., traffic optimization and predictive maintenance), and mobile/enterprise systems. The longstanding issues, such as non-IID data heterogeneity, communication overhead, security threats (poisoning and inference attacks), system heterogeneity, and scalability, are addressed with inventions, such as adaptive aggregation, gradient compression, hierarchical architectures, and Byzantine-robust mechanisms. In recent developments to 2026, the focus of development is toward personalized FL, greater adversarial robustness and connection with large language models, making hybrid and personalized FL the best approach to creating secure, scalable, privacy-preserving analytics in the increasingly decentralized world of big data.