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Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning [article]

Anis Elgabli, Jihong Park, Amrit S. Bedi, Chaouki Ben Issaid, Mehdi Bennis, Vaneet Aggarwal
2020 arXiv   pre-print
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM).  ...  However, due to the lack of centralized entity in decentralized ML, the spatial sparsity and payload compression may incur error propagation, hindering model training convergence.  ...  Exploiting the sheer amount of these user-generated private data is instrumental in training high-accuracy machine learning (ML) models in various domains, ranging from medical diagnosis and disaster/epidemic  ... 
arXiv:1910.10453v6 fatcat:bgyuvk367beibpu47bkfr3zlf4

Overmind: A Collaborative Decentralized Machine Learning Framework

Puttakul Sakul-Ung, Amornvit Vatcharaphrueksadee, Pitiporn Ruchanawet, Kanin Kearpimy, Hathairat Ketmaneechairat, Maleerat Maliyaem
2020 Advances in Science, Technology and Engineering Systems  
data and associated attributes for assigning machine learnings in the collaborative decentralized manner.  ...  This paper introduces "Overmind", the solution that governs and builds the network of decentralized machine learning as a prediction framework named after its functionality: it aims to discover a set of  ...  Overmind: A Framework for Decentralized Machine Learnings Overmind, a collaborative decentralized machine learning framework, is proposed considering dealing with anonymous dataset before building a machine  ... 
doi:10.25046/aj050634 fatcat:gkuh6hqzqvdjpi4uu3s7fr2ufq

Swarm Learning for decentralized and confidential clinical machine learning

Stefanie Warnat-Herresthal, COVID-19 Aachen Study (COVAS), Hartmut Schultze, Krishnaprasad Lingadahalli Shastry, Sathyanarayanan Manamohan, Saikat Mukherjee, Vishesh Garg, Ravi Sarveswara, Kristian Händler, Peter Pickkers, N. Ahmad Aziz, Sofia Ktena (+55 others)
2021 Nature  
Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge  ...  Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3.  ...  The SLL is a framework to enable decentralized training of machine learning models without sharing the data.  ... 
doi:10.1038/s41586-021-03583-3 pmid:34040261 fatcat:5ule2vsgbngltmi6b7ubr24yga

On the Privacy of Decentralized Machine Learning [article]

Dario Pasquini, Mathilde Raynal, Carmela Troncoso
2022 arXiv   pre-print
In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning -- a collaborative machine learning framework aimed at circumventing the main limitations of federated learning  ...  We demonstrate that, contrary to what is claimed by decentralized learning proposers, decentralized learning does not offer any security advantages over more practical approaches such as federated learning  ...  Decentralized machine learning, also known as fullydecentralized machine learning, peer-to-peer machine learning, or gossip learning, aims to address these limitations by performing the optimization learning  ... 
arXiv:2205.08443v1 fatcat:we2manartzgsbgzt3qc54jmpa4

Decentralized Multi-Task Learning Based on Extreme Learning Machines [article]

Yu Ye, Ming Xiao, Mikael Skoglund
2019 arXiv   pre-print
Due to the fact that many data sets of different tasks are geo-distributed, decentralized machine learning is studied.  ...  To exploit the high learning speed of extreme learning machines (ELMs), we apply the ELM framework to the MTL problem, where the output weights of ELMs for all the tasks are learned collaboratively.  ...  DECENTRALIZED MULTI-TASK LEARNING WITH ELM A. Motivation and basics In many real-world applications, the data of different tasks may be geo-distributed over different machines.  ... 
arXiv:1904.11366v1 fatcat:2z3u5d7cynam3k3dliygx72xlm

OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning [article]

Jiacheng Liang, Songze Li, Bochuan Cao, Wensi Jiang, Chaoyang He
2021 arXiv   pre-print
We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications.  ...  Conclusion We develop OmniLytics, the first Ethereum smart contract implementation of a secure data market for decentralized machine learning.  ...  Secure Data Market for Decentralized Machine Learning We consider a network of many compute nodes (e.g., mobile devices like smartphones, or institutions like hospitals, banks, and companies), each of  ... 
arXiv:2107.05252v4 fatcat:u2uaa4fbrvdb3jqbipttcmtvq4

The Non-IID Data Quagmire of Decentralized Machine Learning [article]

Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons
2020 arXiv   pre-print
Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations.  ...  Such datasets pose a significant challenge to decentralized learning because their different contexts result in significant data distribution skew across devices/locations.  ...  Decentralized learning.  ... 
arXiv:1910.00189v2 fatcat:vtlj6cznunavrk57swfedpqdgm

Stochastic Distributed Optimization for Machine Learning from Decentralized Features [article]

Yaochen Hu, Di Niu, Jianming Yang, Shengping Zhou
2019 arXiv   pre-print
We propose an asynchronous stochastic gradient descent (SGD) algorithm for such a feature distributed machine learning (FDML) problem, to jointly learn from decentralized features, with theoretical convergence  ...  We study distributed machine learning from another perspective, where the information about the training same samples are inherently decentralized and located on different parities.  ...  RELATED WORK Distributed Machine Learning.  ... 
arXiv:1812.06415v2 fatcat:r2co4bpg4vfk3mbybe2khcl4gy

Communication-efficient Decentralized Machine Learning over Heterogeneous Networks [article]

Pan Zhou, Qian Lin, Dumitrel Loghin, Beng Chin Ooi, Yuncheng Wu, Hongfang Yu
2020 arXiv   pre-print
In the last few years, distributed machine learning has been usually executed over heterogeneous networks such as a local area network within a multi-tenant cluster or a wide area network connecting data  ...  In these heterogeneous networks, the link speeds among worker nodes vary significantly, making it challenging for state-of-the-art machine learning approaches to perform efficient training.  ...  INTRODUCTION Recently, distributed machine learning has become increasingly popular.  ... 
arXiv:2009.05766v2 fatcat:jpe64nhtwjeaff636kojlxlvla

Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning [article]

Zhixiong Yang, Arpita Gang, Waheed U. Bajwa
2020 arXiv   pre-print
While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual  ...  As a result, we now have a plethora of algorithmic approaches that guarantee robustness of distributed and/or decentralized inference and learning under different adversarial threat models.  ...  Decentralized Machine Learning Decentralized machine learning algorithms, which can be considered a combination of consensus and distributed learning frameworks, approximately solve (2) by minimizing a  ... 
arXiv:1908.08649v2 fatcat:de356dvwinfv5g5njo64qmzpvi

Decentralized machine learning using compressed push-pull averaging

Gábor Danner, István Hegedűs, Márk Jelasity
2020 Proceedings of the 1st International Workshop on Distributed Infrastructure for Common Good  
For decentralized learning algorithms communication efficiency is a central issue. On the one hand, good machine learning models require more and more parameters.  ...  Here, we propose a novel compression mechanism for P2P machine learning that is based on the application of stateful codecs over P2P links.  ...  For example, when it is used as part of a decentralized machine learning platform that runs different learning tasks continuously. Only the second 24 hours are shown in the plots.  ... 
doi:10.1145/3428662.3428792 fatcat:docwsqcrmfgjzdohq7pgnbrthm

Consensus-Based Transfer Linear Support Vector Machines for Decentralized Multi-Task Multi-Agent Learning [article]

Rui Zhang, Quanyan Zhu
2018 arXiv   pre-print
Transfer learning has been developed to improve the performances of different but related tasks in machine learning.  ...  We propose a consensus-based distributed transfer learning framework, where several tasks aim to find the best linear support vector machine (SVM) classifiers in a distributed network.  ...  The proposed framework is a generalization of both centralized transfer learning scheme and distributed machine learning.  ... 
arXiv:1706.05039v2 fatcat:nznequoyhrad3a7s3dfyoiltfe

Learning to Act in Decentralized Partially Observable MDPs

Jilles Steeve Dibangoye, Olivier Buffet
2018 International Conference on Machine Learning  
We address a long-standing open problem of reinforcement learning in decentralized partially observable Markov decision processes.  ...  Experiments show our approach can learn to act near-optimally in many finite domains from the literature.  ...  Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018. Copyright 2018 by the author(s).  ... 
dblp:conf/icml/DibangoyeB18 fatcat:b7wvrytlqjdrhpehyxewkgnziq

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets [article]

David Froelicher, Juan R. Troncoso-Pastoriza, Joao Sa Sousa and Jean-Pierre Hubaux
2020 arXiv   pre-print
Drynx relies on a set of computing nodes to enable the computation of statistics such as standard deviation or extrema, and the training and evaluation of machine-learning models on sensitive and distributed  ...  In this paper, we propose Drynx, a decentralized system for privacy-conscious statistical analysis on distributed datasets.  ...  evaluate machine-learning models on data hosted at different sources, i.e., on distributed datasets.  ... 
arXiv:1902.03785v3 fatcat:jhs2wwxf3jgpxnc7hio7c5xcf4

An Improved Analysis of Gradient Tracking for Decentralized Machine Learning [article]

Anastasia Koloskova, Tao Lin, Sebastian U. Stich
2022 arXiv   pre-print
We consider decentralized machine learning over a network where the training data is distributed across n agents, each of which can compute stochastic model updates on their local data.  ...  [24] on decentralized stochastic gradient descent (D-SGD) has spurred the research on decentralized training methods for machine learning models.  ...  Introduction Methods that train machine learning models on decentralized data offer many advantages over traditional centralized approaches in core aspects such as data ownership, privacy, fault tolerance  ... 
arXiv:2202.03836v1 fatcat:p3oterj35vhpdkshtdodx45udu
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