Optimizing On-Device AI: Overcoming Resource Constraints in Federated Learning for IoT

Abstract

Federated Learning (FL) is revolutionizing privacy in distributed IoT systems by eliminating the need to share raw data. However, it has its challenges. On the client side, attackers can tamper with data or inject false information, leading to what's known as backdoor poisoning attacks. Meanwhile, central servers can compromise data integrity and privacy by manipulating updates and extracting sensitive information from gradients. This is particularly problematic in IoT networks where user privacy is paramount. Innovative techniques like differential privacy and secure aggregation are being developed to tackle these issues and protect user data. Communication and learning convergence also pose significant hurdles due to uneven data distribution and the varied capabilities of IoT devices. To address this, new communication protocols and optimization algorithms are being implemented. Resource management is another critical area, given the limited computational power of many IoT devices. Solutions like resource-aware FL architectures and optimized AI models are emerging to ease these constraints. Additionally, advancements in AI hardware and lightweight training strategies are making deploying AI on IoT sensors more feasible. Finally, adopting standards such as ETSI Multi-access Edge Computing (MEC) and modern communication protocols is essential for the widespread deployment of FL-IoT systems, ensuring they are secure, efficient, and interoperable.