We present afederated learningframework for real-time emotion state classification from multimodal data streaming (Fed-ReMECS). • The data streams are generated at high rate from various physiological measurements (EDA, RB, EEG, etc.) of different users. ...
Federated Learning for Data Streams (Arxiv 2023)[paper] FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge (Arxiv 2022)[paper] No One Left Behind: Real-World Federated Class-Incremental Learning (Arxiv 2023)[paper][code] ...
social golang linked-data json-ld golang-library federated activitystreams activitypub activitystreams-vocabulary Updated Dec 4, 2022 Go bonfire-networks / bonfire-app Sponsor Star 621 Code Issues Pull requests Bonfire - tend to your digital life in community. Customise and host your own online...
Federated learning is one of the most promising distributed machine learning frameworks because it supports data privacy and security by not sharing the clients' data but instead sharing their local models. In federated learning, many clients explicitly train their machine learning/deep learning models ...
Federated Learning Data heterogeneity Non-IID data Concept drift Distributed learning Continual learning 1. Introduction Machine Learning (ML) consists of the study of mathematical algorithms that improve automatically through experience with the use of data. Traditionally, data used for training ML algorit...
Traditional clustering algorithms target offline datasets, while client updates in federated learning are continuous data streams. To address this, our method analyzes client parameter trajectories over time and uses the Isolation Forest anomaly detection algorithm to detect dynamic trends and potential ...
Non-IID). The client selection is a process of selecting relevant nodes to start the training using local data. The selected node then acquires the current model and waits for the next model. Node dropping is the dropping of too-slow learning nodes from the cooperative machine learning models...
2018年fb的文章Federated meta-learning for recommendation的阅读笔记 想法 用元学习的方法解决少数据的问题,并用差分隐私保护用户的隐私性 这篇文章作者声称有两点创新,一是meta-learning在算法层面,二是用联邦学习保护用户隐私。但文章中的算法A与之前工作没有任何区别,都是模型的初始化权重,也就是meta-learner。总...
Cps data streams analytics based on machine learning for cloud and fog computing: a survey Future Generat. Comput. Syst. (2019) P. Li et al. Multi-key privacy-preserving deep learning in cloud computing Future Generat. Comput. Syst. (2017) M. Abadi et al. Deep learning with differential ...
- 《IEEE Transactions on Knowledge & Data Engineering》 被引量: 0发表: 0年 Continual three-way decisions via knowledge transfer credit risk prediction, updates the model from scratch for time-changing data streams, resulting in significant computational, time, and storage consumptio... YangXin,Wu...