本地微调(Local fine tuning):本地微调即每个客户端接收到一个全局模型,并使用自己的局部数据和几个梯度下降步骤对其进行调优,这种方法主要结合了元学习。 多任务学习(multi_task learning):对个性化问题的另一种观点是视为多任务学习问题。这种设置下对每个客户端的优化可以看做是一个新的任务。 情景化(Contextualiz...
论文笔记:ICML'21 SpreadGNN: Serverless Multi task Federated Learning for Graph Neural Networks 天下客 机器学习、联邦学习、图神经网络14 人赞同了该文章 前言 GNN 是分析解决图机器学习问题的首选方法,但是由于用户方的隐私问题、法规限制和商业竞争等原因,集中大量的真实世界图数据用于GNN的训练是很困难的。
First efforts in FL focused on learning a single global model with good average performance across clients, but the global model may be arbitrarily bad for a given client, due to the inherent heterogeneity of local data distributions. Federated multi-task learning (MTL) approaches can learn ...
Considering the effect of statistical heterogeneity, work [29] proposed a novel federated multitask learning (FMTL) framework that forms clusters of clients based on the geometric properties of the FL surface with jointly trainable data distribution. This clustering approach provided better results in ...
code: https://github.com/felisat/clustered-federated-learning 编辑:古月 目的:传统联邦学习中,存在这个假设: 训练一组模型,使得所有得到用户都能满足最小化风险函数的目标: 假设1 但是,显然这种假设并不是对于所有用户都满足,因为两个用户之间的数据分布很容易是很不相似的,对应于FL中的Non_IID问题。
multi-task federated learning for personalised Multi-task federated learning for personalized healthcare Multi-task federated learning (MTFL) is an emerging approach in personalized healthcare that combines the benefits of federated learning and multi-task learning. Federated learning enables the training ...
The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model training on multi-task learning datasets. However, a comprehensive evaluation method, integrating the unique features of both FL...
我是一颗小蘑菇: 新鲜出炉的ICML2021的论文,趁热读了(因为后天开组会hhh) 原来的题目Federated Multi-Task Learning for Competing Constraint…阅读全文 赞同88 26 条评论 分享收藏 [论文笔记]个性化联邦学习 Towards Personalized Federated Learning 白小鱼 机器学习等 2 个话题下的优秀...
Similarly, Yu et al. (2020) build a federated multi- task learning framework for smart home IOT to automatically learn users’ behavior patterns, which could effectively detect physical hazards. Furthermore, Liu, Wang, Liu, and Xu (2020) proposed a data fusion approach based on FL for robots...
This is the code for paper : Accelerating communication-efficient federated multi-task learning with personalization and fairness. we proposes a local momentum based method FedMGDA-M, FedAvg-M , and its derivatives FedAvg-M-Mom, FedMGDA-M-Mom. Besides, we compared them with other accelerated met...