SLA-Driven Adaptive FL policy with Real-Time Visualization for Zero-Touch 6G Network Slicing

6G paradigm enables massive network slicing for pervasive digitization across vertical industries, demanding scalable,sustainable, AI-driven zero-touch automation, particularly under non-IID conditions in live networks. This work introducesa cloud-native service-level agreement (SLA)-driven stochastic policy to guarantee a scalable and fast operation of constrainedfederated learning (FL)-based analytic engines (AE) that perform statistical slice-level resource provisioning at RAN-Edge domain, de