Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning最新文献

Towards an Efficient System for Differentially-private, Cross-device Federated Learning 一种有效的差分私有、跨设备联邦学习系统

Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning Pub Date : 2021-10-25 DOI: 10.1145/3477114.3488762

Kunlong Liu, Richa Wadaskar, Trinabh Gupta

引用次数: 0

Community-Structured Decentralized Learning for Resilient EI 弹性情商的社区结构分散式学习

Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning Pub Date : 2021-10-25 DOI: 10.1145/3477114.3488764

Harikrishna S. Kuttivelil, K. Obraczka

引用次数: 0

GradSec

Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning Pub Date : 2021-10-25 DOI: 10.1145/3477114.3488763

Aghiles Ait Messaoud, Sonia Ben Mokhtar, Vlad Nitu, V. Schiavoni

引用次数: 0

FedScale FedScale

Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning Pub Date : 2021-10-25 DOI: 10.1145/3477114.3488760

Fan Lai, Yinwei Dai, Xiangfeng Zhu, H. Madhyastha, Mosharaf Chowdhury

{"title":"FedScale","authors":"Fan Lai, Yinwei Dai, Xiangfeng Zhu, H. Madhyastha, Mosharaf Chowdhury","doi":"10.1145/3477114.3488760","DOIUrl":"https://doi.org/10.1145/3477114.3488760","url":null,"abstract":"We present FedScale, a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research. FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, language modeling, speech recognition, and reinforcement learning. For each dataset, we provide a unified evaluation protocol using realistic data splits and evaluation metrics. To meet the pressing need for reproducing realistic FL at scale, we have also built an efficient evaluation platform to simplify and standardize the process of FL experimental setup and model evaluation. Our evaluation platform provides flexible APIs to implement new FL algorithms and includes new execution backends with minimal developer efforts. Finally, we perform indepth benchmark experiments on these datasets. Our experiments suggest fruitful opportunities in heterogeneityaware co-optimizations of the system and statistical efficiency under realistic FL characteristics. FedScale is open-source with permissive licenses and actively maintained,1 and we welcome feedback and contributions from the community.","PeriodicalId":288476,"journal":{"name":"Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124348254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Redundancy in cost functions for Byzantine fault-tolerant federated learning 拜占庭容错联邦学习中代价函数的冗余

Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning Pub Date : 2021-10-25 DOI: 10.1145/3477114.3488761

Shuo Liu, Nirupam Gupta, N. Vaidya

引用次数: 1

Separation of Powers in Federated Learning (Poster Paper) 联邦学习中的权力分立(海报纸)

Proceedings of the First Workshop on Systems Challenges in Reliable and Secure Federated Learning Pub Date : 2021-05-19 DOI: 10.1145/3477114.3488765

P. Cheng, Kevin Eykholt, Zhongshu Gu, H. Jamjoom, K. R. Jayaram, Enriquillo Valdez, Ashish Verma

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引用次数: 7