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Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization
In this paper, we present a system design which offers efficient protection of individual model updates throughout the learning procedure, allowing clients to only provide obscured model updates while a cloud server can still perform the aggregation.
Enabling Efficient Privacy-Assured Outlier Detection over Encrypted Incremental Datasets
It realises several cryptographic modules via efficient and interchangeable protocols to support the above cryptographic operations and composes them in the overall protocol to enable outlier detection over encrypted datasets.
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