Our paper proposes a novel multi-center aggregation mechanism for federated learning, which learns multiple global models from the non-IID user data and simultaneously derives the optimal matching between users and centers. We formulate the problem as a joint optimization that can be efficiently ...
With Acuratio's Multicloud Federated Learning Platform organizations around the world are able to unlock the value of data by combining datasets without compromising privacy
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作者说这个很像Multi-objective minimization (MoM)中的 Chebyshev approach,于是提出如下iterative的算法,这里adaptively “center” the user functions using function values from the previous iteration. 当函数f是光滑的时候,用quadratic bound得到: 强对偶性,转化为对偶问题 因此,转化为计算iteratively计算如下问题: ...
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 ...
During the training process of federated learning, if we design the global model to shift in the direction of the local model far away from it, we find that the global model has higher test accuracy and lower accuracy variance within a certain step size range. Fig. 1 illustrates the variati...
传统的联邦学习(Federated Learning, FL)通常采用星型拓扑结构,客户端直接与中心服务器通信以同步模型。然而,在现实世界的分布式系统中,往往存在层次化的架构。 层次化联邦学习(HFL)作为连接传统联邦学习和现实分布式系统架构的桥梁,通过在系统多个层级上利用聚合点。 问题挑战: 现有的HFL算法在处理多时间尺度模型漂移(...
Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among...
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。
Federated learning (FL) offers a privacy-centric distributed learning framework, enabling model training on individual clients and central aggregation without necessitating data exchange. Nonetheless, FL implementations often suffer from non-i.i.d. and long-tailed class distributions across mobile applicatio...