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Found 4 papers, 2 papers with code
Truncated Non-Uniform Quantization for Distributed SGD
To address the communication bottleneck challenge in distributed learning, our work introduces a novel two-stage quantization strategy designed to enhance the communication efficiency of distributed Stochastic Gradient Descent (SGD).
Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation
Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations.
Task Adaptive Network for Image Restoration With Combined Degradation Factors
Therefore, we propose a task-adaptive attention module to enable the network to restore images with multiple degradation factors.
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost
Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance.
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