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Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation
Previous attacks assume the adversary can infer the local learning rate of each client, while we observe that: (1) using the uniformly distributed random local learning rates does not incur much accuracy loss of the global model, and (2) personalizing local learning rates can mitigate the drift issue which is caused by non-IID (identically and independently distributed) data.
Probabilistic Modeling and Inference for Obfuscated Cyber Attack Sequences
A key element in defending computer networks is to recognize the types of cyber attacks based on the observed malicious activities.
Cryptography and Security
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