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Found 7 papers, 2 papers with code
FedPerm: Private and Robust Federated Learning by Parameter Permutation
We present FedPerm, a new FL algorithm that addresses both these problems by combining a novel intra-model parameter shuffling technique that amplifies data privacy, with Private Information Retrieval (PIR) based techniques that permit cryptographic aggregation of clients' model updates.
Subject Granular Differential Privacy in Federated Learning
We formally prove the subject level DP guarantee for our algorithms, and also show their effect on model utility loss.
Subject Membership Inference Attacks in Federated Learning
Using these attacks, we estimate subject membership inference risk on real-world data for single-party models as well as FL scenarios.
Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions
Users can indirectly contribute to, and directly benefit from a much larger aggregate data corpus used to train the global model.
Microsecond Consensus for Microsecond Applications
We propose Mu, a system that takes less than 1. 3 microseconds to replicate a (small) request in memory, and less than a millisecond to fail-over the system - this cuts the replication and fail-over latencies of the prior systems by at least 61% and 90%.
Distributed, Parallel, and Cluster Computing
Efficient Multi-word Compare and Swap
Then we present the first algorithm that requires k+1 CASes per call to k-CAS in the common uncontended case.
Distributed, Parallel, and Cluster Computing
Private Federated Learning with Domain Adaptation
Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy.
