Search Results for author:
Found 7 papers, 1 papers with code
Feature Matching Data Synthesis for Non-IID Federated Learning
For better privacy preservation, we propose a hard feature augmentation method to transfer real features towards the decision boundary, with which the synthetic data not only improve the model generalization but also erase the information of real features.
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning
More specifically, we decouple a NN model into a personalized feature extractor, obtained by aggregating models from similar clients, and a classifier, which is obtained by local training and used to estimate client similarity.
Federated Learning with GAN-based Data Synthesis for Non-IID Clients
A combination of the local private dataset and synthetic dataset with confident pseudo labels leads to nearly identical data distributions among clients, which improves the consistency among local models and benefits the global aggregation.
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity
By exploiting the low-latency communication among edge servers for efficient model sharing, SD-FEEL can incorporate more training data, while enjoying much lower latency compared with conventional federated learning.
Decentralized Stochastic Proximal Gradient Descent with Variance Reduction over Time-varying Networks
By transforming our decentralized algorithm into a centralized inexact proximal gradient algorithm with variance reduction, and controlling the bounds of error sequences, we prove that DPSVRG converges at the rate of $O(1/T)$ for general convex objectives plus a non-smooth term with $T$ as the number of iterations, while DSPG converges at the rate $O(\frac{1}{\sqrt{T}})$.
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large communication latency in Cloud-based machine learning solutions, while preserving data privacy.
Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach
In this paper, we develop multi-armed bandit approaches for more efficient detection via coordinate descent, which make a delicate trade-off between exploration and exploitation in coordinate selection.
