Federated Learning Across Hybrid-Cloud Environments: Privacy-Preserving Model
DOI:
https://doi.org/10.15662/IJRAI.2025.0803011Keywords:
Federated Learning, Multi-Cloud Orchestration, Privacy Preservation, Secure Aggregation, Differential Privacy, Azure, On-Premises, 3PC CloudAbstract
Federated learning (FL) enables collaborative model training without sharing raw data, which is ideal for telecom applications spanning on-premises datacenters, Azure, and third-party (3PC) clouds. However, coordinating FL across heterogeneous clouds introduces challenges in privacy preservation, communication overhead, and orchestration. We present MultiCloud-FL, a framework that orchestrates training rounds across three environments, employs secure aggregation and differential privacy to protect client updates, and adapts to variable network latencies.
In experiments training an image-classification model on a synthetic telecom dataset partitioned across environments, MultiCloud-FL achieved:
• 94.2 % accuracy (vs. 95.0 % centralized)
• 35 % reduction in inter-cloud communication compared to vanilla FL.
• Differential-privacy (1.0) with 1.5 % accuracy loss
We detail system architecture, secure protocols, mermaid diagrams, quantitative evaluation, and discuss limitations and future directions.
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