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 with differential privacy: Algorithms and performance analysis
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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.
- Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation