《No fear of heterogeneity: Classifier calibration for federated learning with non-iid data》发表在 2021 年的机器学习顶级会议 NeurIPS 上 [1],此篇论文旨在解决联邦学习客户端数据分布异构的问题,并设计了一个简洁且优美的分类器矫正方法。此篇论文的叙事非常巧妙,通过实验观察一步一步引出创新点,并且所提方法...
Knowledge-Aware Federated Active Learning with Non-IID DataAbstract联合学习使多个分散的客户端能够在不共享本地训练数据的情况下进行协作学习。然而,在本地客户端上获取数据标签的昂贵注释成本仍然是利用本…
Federated Learning with Non-IID Data 论文中分析了FedAvg算法在Non-IID数据时,准确率下降的原因。并提出共享5%的数据可提高准确率。 Federated Learning with Non-IID Data Abstract 联邦学习支持边缘受限的计算设备(例如移动电话和物联网设备)在保持训练本地数据的同时共享模型。用去中心化的方法训练模型保障了隐私 ...
L−smooth is the only required condition and their analysis is based on that solvingWkis feasible. As discussed inmy previous bolg, we should proveWkis convergent toW∗along withk→∞. According to the definition of strongly convex, we have ...
Non-IID Data Federated Learning with Non-IID Data IID: 独立同分布 (idependently and identically distributed, IID) 论文链接 Abstract 联合学习使资源受限的边缘计算设备(例如移动电话和IoT设备)能够学习共享的预测模型,同时将训练数据保持在本地。这种去中心化的训练模型方法提供了隐私,安全性,监管和经济利益。
12 p. EgoMimic: Scaling Imitation Learning via Egocentric Video 33 p. Bridging Geometric States via Geometric Diffusion Bridge 23 p. Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning 18 p. Teaching Embodied Reinforcement Learning Agents: Informativeness and Div...
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data 论文阅读笔记,程序员大本营,技术文章内容聚合第一站。
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving t... X Zhang,M Hong,S Dhople,... - arXiv 被引量...
1.1 Overcoming Forgetting in Sequential Lifelong Learning and in Federated Learning 联邦学习问题和另一个被称为终身学习的基本机器学习问题(以及相关的多任务学习)之间有着很深的相似性。在终身学习中,挑战是学习任务A,并使用相同的模型继续学习任务B,但不要“遗忘”,不要严重影响任务A的性能;或者一般来说,学习...
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this ...