Federated learning for edge artificial intelligence: Enhancing security, robustness, privacy, personalization, and blockchain integration in IoT

Abstract

In order to enable edge artificial intelligence (AI) in Internet of Things (IoT) ecosystems, federated learning (FL) has emerged as a game-changing technique that addresses important issues like data privacy, security, robustness, and personalization. In contrast to conventional AI models that depend on centralized data gathering, FL allows edge devices to work together to jointly learn a shared model while maintaining localized data, greatly improving privacy and lowering transmission overhead. However, there are special difficulties when integrating FL with IoT, including heterogeneity in edge devices, a lack of computational power, and susceptibility to security breaches. This research investigates state-of-the-art developments in FL for edge AI, with an emphasis on strengthening security and resilience against adversarial attacks like model inversion and data poisoning. To guarantee that private information is kept safe, privacy-preserving methods like homomorphic encryption and differential privacy are examined. Furthermore, the study explores personalization techniques that enable FL models to adjust to the unique needs of individual IoT devices, enhancing system performance and user experience. The research also discusses how blockchain technology can be integrated into FL systems to improve their security and reliability.