Search Results for author:
Found 7 papers, 1 papers with code
Hyperparameter Optimization through Neural Network Partitioning
Well-tuned hyperparameters are crucial for obtaining good generalization behavior in neural networks.
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices
We perform extensive experiments on standard FL benchmarks to evaluate our proposed FedAvg variants for quantization robustness and provide a convergence analysis for our Quantization-Aware variants in FL.
An Expectation-Maximization Perspective on Federated Learning
Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device.
DP-REC: Private & Communication-Efficient Federated Learning
Privacy and communication efficiency are important challenges in federated training of neural networks, and combining them is still an open problem.
Federated Mixture of Experts
Federated learning (FL) has emerged as the predominant approach for collaborative training of neural network models across multiple users, without the need to gather the data at a central location.
Federated Averaging as Expectation Maximization
Federated averaging (FedAvg), despite its simplicity, has been the main approach in training neural networks in the federated learning setting.
Relaxed Quantization for Discretized Neural Networks
Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices.
