Learning to Compare: Relation Network for Few-Shot Learning
18年CVPR,针对小样本领域的学习框架
Client-Edge-Cloud Hierarchical Federated Learning
新思路，主要是理论分析头疼
A Comprehensive Introduction to Label Noise
一篇噪声标签的短篇综述
Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing
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0x00 Abstract
A main challenge of FL is that the training data are usually non-Independent, Identically Distributed (non-IID) on the clients, which may bring the biases in the model training and cause possible accuracy degradation. To address this issue, this paper aims to propose a novel FL algorithm to alleviate the accuracy degradation caused by non-IID data at clients. Firstly, we observe that the clients with different degrees of non-IID data present heterogeneous weight divergence wit ...
Active Federated Learning
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0x00 ABSTRACT
The data on each client is highly variable, so the benefit of training on different clients may differ dramatically. To exploit this we propose Active Federated Learning, where in each round clients are selected not uniformly at random, but with a probability conditioned on the current model and the data on the client to maximize efficiency.
Key Words: Active Learning, Federated Learning
0x01 Introduction
In this paper we introduce Active Federated Learning (AFL) to preferenti ...
