6G-Integrated Federated Learning for Trustworthy and Scalable Edge-IoT Collaboration

Abstract

The intersection of sixth-generation (6G) communication networks, edge computing, and federated learning offers a novel occasion regarding empowering trustful and scalable collaboration throughout distributed Internet of Things (IoT) ecosystems. Conventional centralized machine learning technology is plagued by severe constraints in IoT scenes, such as privacy issues, network congestion, and network bottlenecks. The paper has presented a new 6G-integrated federated learning system which builds on the native intelligence of 6G networks to support secure, efficient, and scalable cooperative learning among heterogeneous edge-IoT devices. The suggested architecture combines terahertz frequencies to synchronize model communication in real-time at ultra-low latency, reconfigurable intelligent surfaces to improve the quality of communication, and network slicing to provide differentiated quality-of-service assurances. Another new hierarchical federated learning system integrates intra-edge aggregation and inter-edge cooperation that can reduce communication overhead by 85-percent and achieve the same accuracy in models. This framework integrates blockchain-based trust management involving zero-knowledge proofs of verifiable model update, to provide integrity and accountability without impacting on privacy. Experimental analysis of massive scale edge-IoT applications has revealed that the suggested scheme attains 97.2% model precision and lowers communication expenses by 87 percent and convergence rate by 3.4 times that of traditional federated learning techniques. The framework has high-uniform performance in adversarial environments where 99.6 percent of malicious model updates are identified with a small false positive. The results define the 6G-integrated federated learning as a framework of reliable and scalable edge-IoT cooperation.