EdgeDetect delivers 98% multi-class accuracy in federated intrusion detection with 32x gradient compression via median binarization and homomorphic encryption, cutting per-round communication from 450 MB to 14 MB.
Federated learning on non-iid data: A survey
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The paper surveys technical requirements, use cases, challenges, and future trends for building brain-computer interfaces on top of 6G wireless networks.
citing papers explorer
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EdgeDetect: Importance-Aware Gradient Compression with Homomorphic Aggregation for Federated Intrusion Detection
EdgeDetect delivers 98% multi-class accuracy in federated intrusion detection with 32x gradient compression via median binarization and homomorphic encryption, cutting per-round communication from 450 MB to 14 MB.
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Toward 6G-enabled Brain Computer Interfaces: Technical Requirements, Use Cases, Challenges, and Future Trends
The paper surveys technical requirements, use cases, challenges, and future trends for building brain-computer interfaces on top of 6G wireless networks.