Nonlinear Adaptive Federated Learning with Privacy Preservation for Edge-Cloud Systems

Abstract

The rapid-fire proliferation of edge- cloud systems has steered in unknown openings for cooperative intelligence while introducing critical challenges in data sequestration, diversity, and non-linear rigidity. This paper presents a new frame for Nonlinear Adaptive Federated Learning with sequestration Preservation acclimatized to edge- pall surroundings. The proposed approach integrates three vital methods Noise Addition for sequestration during preprocessing to ensure discrimination sequestration without compromising data utility; Privacy- Preserving Feature Selection for relating significant features while securing sensitive information; and Federated Generative Adversarial Networks (FedGANs) for robust, adaptive bracket that captures complex, on-linear patterns across miscellaneous datasets. The system leverages decentralized literacy to address. data distributions and employs inimical mechanisms to enhance model conception and delicacy. Experimental evaluations demonstrate significant advancements in sequestration, computational effectiveness, and model performance compared to traditional federated learning methods. This exploration underscores the eventuality of integrating advanced sequestration- conserving and adaptive ways to enable secure and scalable intelligence in edge- pall ecosystems.