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Found 5 papers, 3 papers with code
QUIC-FL: Quick Unbiased Compression for Federated Learning
Distributed Mean Estimation (DME), in which $n$ clients communicate vectors to a parameter server that estimates their average, is a fundamental building block in communication-efficient federated learning.
SDR: Efficient Neural Re-ranking using Succinct Document Representation
BERT based ranking models have achieved superior performance on various information retrieval tasks.
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
Distributed Mean Estimation (DME) is a central building block in federated learning, where clients send local gradients to a parameter server for averaging and updating the model.
DRIVE: One-bit Distributed Mean Estimation
We consider the problem where $n$ clients transmit $d$-dimensional real-valued vectors using $d(1+o(1))$ bits each, in a manner that allows the receiver to approximately reconstruct their mean.
Towards Federated Learning With Byzantine-Robust Client Weighting
Federated Learning(FL) is a distributed machine learning paradigm where data is distributed among clients who collaboratively train a model in a computation process coordinated by a central server.
