A federated learning framework with homomorphic encryption and dynamic agent selection detects anomalies in IIoT while preserving privacy and reducing communication bottlenecks.
Partially encrypted multi- party computation for federated learning
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The paper surveys split and aggregation learning for foundation models in 6G networks to improve efficiency, resource use, and data privacy in distributed AI.
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Towards Securing IIoT: An Innovative Privacy-Preserving Anomaly Detector Based on Federated Learning
A federated learning framework with homomorphic encryption and dynamic agent selection detects anomalies in IIoT while preserving privacy and reducing communication bottlenecks.
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Split and Aggregation Learning for Foundation Models Over Mobile Embodied AI Network (MEAN): A Comprehensive Survey
The paper surveys split and aggregation learning for foundation models in 6G networks to improve efficiency, resource use, and data privacy in distributed AI.