Orchestrating Secure Federated Intelligence Across 6G Edge Networks

The next generation of mobile networks is moving artificial intelligence out of a sporadic implementation model and into the managed service paradigm, which requires careful placement, scaling, validation and security of the radio, edge and cloud space. Although federated learning is vulnerable to meet strict privacy requirements, its training process has the properties of a functional service chain, which includes transient participation and intermittent client interactions, competitive service slices, and the need to implement trust and comply with latency and reliability requirements. Secure federated intelligence is described as an orchestrated 6G-optimized service graph in this research, which includes: (i) slice-aware admission and resource assignment; (ii) trust-gated robust aggregation; and (iii) post-quantum secure update exchange as composable tasks. An orchestration workflow with a feasibility-first constraint is planned to maintain constraints at the service level and also ensure the convergence in case of heterogeneous participation and adversity of poisoning. Large scale simulation of three different 6G slice formats illustrate a steady decrease in tail latency and SLA breaches, an improved resistance to malicious clients, and a relatively low control overhead, thus supporting the feasibility of the federated modelling to federated managed service transition.