Adaptive privacy-preserving federated learning for robust IoT systems: A defense against data poisoning attacks

Federated Learning (FL) is a machine learning paradigm that enables collaborative model training across multiple devices, such as those found in the Internet of Things (IoT), while preserving data privacy. Despite its potential, FL is vulnerable to attacks, including data poisoning. This paper introduces an Adaptive Privacy-Preserving FL (APPFL) method that helps mitigate these risks. APPFL adjusts the influence of clients by adaptively weighting each update, ensuring that the contribution of each client is dynamically adjusted to improve accuracy. It incorporates local differential privacy to enhance individual data privacy further. The efficacy of APPFL is evaluated using simulated IoT devices and various datasets, including MNIST and CIFAR-10, demonstrating its robustness against poisoning attacks and its ability to maintain privacy. This research contributes to the ongoing efforts to secure FL, a critical technology in today’s data-driven industries.