Proceedings of the 1st Workshop on Distributed Machine Learning最新文献

FLaaS

Proceedings of the 1st Workshop on Distributed Machine Learning Pub Date : 2020-12-01 DOI: 10.1145/3426745.3431337

N. Kourtellis, Kleomenis Katevas, Diego Perino

{"title":"FLaaS","authors":"N. Kourtellis, Kleomenis Katevas, Diego Perino","doi":"10.1145/3426745.3431337","DOIUrl":"https://doi.org/10.1145/3426745.3431337","url":null,"abstract":"Federated Learning (FL) is emerging as a promising technology to build machine learning models in a decentralized, privacy-preserving fashion. Indeed, FL enables local training on user devices, avoiding user data to be transferred to centralized servers, and can be enhanced with differential privacy mechanisms. Although FL has been recently deployed in real systems, the possibility of collaborative modeling across different 3rd-party applications has not yet been explored. In this paper, we tackle this problem and present Federated Learning as a Service (FLaaS), a system enabling different scenarios of 3rd-party application collaborative model building and addressing the consequent challenges of permission and privacy management, usability, and hierarchical model training. FLaaS can be deployed in different operational environments. As a proof of concept, we implement it on a mobile phone setting and discuss practical implications of results on simulated and real devices with respect to on-device training CPU cost, memory footprint and power consumed per FL model round. Therefore, we demonstrate FLaaS's feasibility in building unique or joint FL models across applications for image object detection in a few hours, across 100 devices.","PeriodicalId":301937,"journal":{"name":"Proceedings of the 1st Workshop on Distributed Machine Learning","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125517405","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: 4

Huffman Coding Based Encoding Techniques for Fast Distributed Deep Learning 基于Huffman编码的快速分布式深度学习编码技术

Proceedings of the 1st Workshop on Distributed Machine Learning Pub Date : 2020-12-01 DOI: 10.1145/3426745.3431334

Rishikesh R. Gajjala, Shashwat Banchhor, A. Abdelmoniem, Aritra Dutta, M. Canini, Panos Kalnis

引用次数: 18

Accelerating Intra-Party Communication in Vertical Federated Learning with RDMA 利用RDMA加速垂直联邦学习中的党内交流

Proceedings of the 1st Workshop on Distributed Machine Learning Pub Date : 2020-12-01 DOI: 10.1145/3426745.3431333

Duowen Liu

{"title":"Accelerating Intra-Party Communication in Vertical Federated Learning with RDMA","authors":"Duowen Liu","doi":"10.1145/3426745.3431333","DOIUrl":"https://doi.org/10.1145/3426745.3431333","url":null,"abstract":"Federated learning (FL) has emerged as an elegant privacy-preserving distributed machine learning (ML) paradigm. Particularly, vertical FL (VFL) has a promising application prospect for collaborating organizations owning data of the same set of users but with disjoint features to jointly train models without leaking their private data to each other. As the volume of training data and the model size increase rapidly, each organization may deploy a cluster of many servers to participant in the federation. As such, the intra-party communication cost (i.e., network transfers within each organization's cluster) can significantly impact the entire VFL job's performance. Despite this, existing FL frameworks use the inefficient gRPC for intra-party communication, leading to high latency and high CPU cost. In this paper, we propose a design to transmit data with RDMA for intra-party communication, with no modifications to applications. To improve the network efficiency, we further propose an RDMA usage arbiter to adjust the RDMA bandwidth used for a non-straggler party dynamically, and a query data size optimizer to automatically find out the optimal query data size that each response carries. Our preliminary results show that RDMA based intra-party communication is 10x faster than gRPC based one, leading to a reduction of 9% on the completion time of a VFL job. Moreover, the RDMA usage arbiter can save over 90% bandwidth, and the query data size optimizer can improve the transmission speed by 18%.","PeriodicalId":301937,"journal":{"name":"Proceedings of the 1st Workshop on Distributed Machine Learning","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133360947","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: 3

FEWER 更少的

Proceedings of the 1st Workshop on Distributed Machine Learning Pub Date : 2020-12-01 DOI: 10.1145/3426745.3431335

Yongjin Shin, Gihun Lee, Seungjae Shin, Se-Young Yun, Il-Chul Moon

引用次数: 1

Maggy 玛吉

Proceedings of the 1st Workshop on Distributed Machine Learning Pub Date : 2020-12-01 DOI: 10.1145/3426745.3431338

Moritz Meister, Sina Sheikholeslami, A. H. Payberah, Vladimir Vlassov, J. Dowling

引用次数: 8

Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions 内容传递系统中的神经增强:最新技术和未来方向

Proceedings of the 1st Workshop on Distributed Machine Learning Pub Date : 2020-10-12 DOI: 10.1145/3426745.3431336

Royson Lee, Stylianos I. Venieris, N. Lane

引用次数: 6