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Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums [article]

Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan
2022 arXiv   pre-print
We present RESAM (RESilient Averaging of Momentums), a unified framework that makes it simple to establish optimal Byzantine resilience, relying only on standard machine learning assumptions.  ...  Byzantine resilience emerged as a prominent topic within the distributed machine learning community.  ...  John is partly supported by SNSF project 200021_182542, machine learning. Rafaël is partly supported by an Ecocloud postdoctoral fellowship.  ... 
arXiv:2205.12173v1 fatcat:hqh4cbzdkjegxfn6prqajti76u

Byzantine Fault Tolerance in Distributed Machine Learning : a Survey [article]

Djamila Bouhata, Hamouma Moumen
2022 arXiv   pre-print
Byzantine Fault Tolerance (BFT) is among the most challenging problems in Distributed Machine Learning (DML).  ...  Byzantine failures are still difficult to tackle due to their unrestricted nature; as a result, the possibility of generating arbitrary data.  ...  [101] studied the issue of Byzantine-resilient distributed machine learning in a decentralized architecture.  ... 
arXiv:2205.02572v1 fatcat:h2hkcgz3w5cvrnro6whl2rpvby

Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing [article]

Sai Praneeth Karimireddy, Lie He, Martin Jaggi
2022 arXiv   pre-print
In Byzantine robust distributed or federated learning, a central server wants to train a machine learning model over data distributed across multiple workers.  ...  However, a fraction of these workers may deviate from the prescribed algorithm and send arbitrary messages.  ...  This project was supported by SNSF grant 200020_200342.  ... 
arXiv:2006.09365v5 fatcat:aeqfsarizfgflkw2dxkwi44peu

Learning from History for Byzantine Robust Optimization [article]

Sai Praneeth Karimireddy, Lie He, Martin Jaggi
2021 arXiv   pre-print
Byzantine robustness has received significant attention recently given its importance for distributed and federated learning.  ...  First, we show realistic examples where current state of the art robust aggregation rules fail to converge even in the absence of any Byzantine attackers.  ...  We are partly supported by a Google Focused Research Award.  ... 
arXiv:2012.10333v3 fatcat:vgpkiqzb75aetc3ipaniocjvmu

SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification [article]

Ashwinee Panda, Saeed Mahloujifar, Arjun N. Bhagoji, Supriyo Chakraborty, Prateek Mittal
2021 arXiv   pre-print
We propose a theoretical framework for analyzing the robustness of defenses against poisoning attacks, and provide robustness and convergence analysis of our algorithm.  ...  In model poisoning attacks, the attacker reduces the model's performance on targeted sub-tasks (e.g. classifying planes as birds) by uploading "poisoned" updates.  ...  B.6.3 Byzantine attacks Prior work has evaluated model poisoning attacks with the objective of inducing Byzantine failure against the same cadre of Byzantine-resilient aggregation rules .  ... 
arXiv:2112.06274v1 fatcat:btoycf5nojgzferdjgwumtpvdi

Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions [article]

Alberto Blanco-Justicia, Josep Domingo-Ferrer, Sergio Martínez, David Sánchez, Adrian Flanagan, Kuan Eeik Tan
2020 arXiv   pre-print
Federated learning (FL) allows a server to learn a machine learning (ML) model across multiple decentralized clients that privately store their own training data.  ...  However, FL is not free of issues. On the one hand, the model updates sent by the clients at each training epoch might leak information on the clients' private data.  ...  The authors from URV are with the UNESCO Chair in Data Privacy, but the views in this paper are their own and are not necessarily shared by UNESCO.  ... 
arXiv:2012.06810v1 fatcat:ipqvco22uraepf7ygmzfso3yy4

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks [article]

Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang, Oscar Villemaud
2022 arXiv   pre-print
impossibility theorems on Byzantine machine learning.  ...  To study the resilience of distributed learning, the "Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server.  ...  The authors are thankful to the anonymous reviewers of ICLR 2022 and ICML 2022 for their constructive comments.  ... 
arXiv:2202.08578v2 fatcat:i2hpyudszjhanbu2syiskqyzky

Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications [article]

S. Hu, X. Chen, W. Ni, E. Hossain, X. Wang
2020 arXiv   pre-print
Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications.  ...  This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks.  ...  Next, BYRDIE uses the Byzantine-resilient approach to solve each scalar-valued subproblem.  ... 
arXiv:2012.01489v1 fatcat:pdauhq4xbbepvf26clhpqnc2ci

Byzantine-Resilient SGD in High Dimensions on Heterogeneous Data [article]

Deepesh Data, Suhas Diggavi
2020 arXiv   pre-print
We also propose and analyze a Byzantine-resilient SGD algorithm with gradient compression, where workers send k random coordinates of their gradients.  ...  At the core of our algorithm, we use the polynomial-time outlier-filtering procedure for robust mean estimation proposed by Steinhardt et al. (ITCS 2018) to filter-out corrupt gradients.  ...  Master (denoted by M) wants to learn a machine learning model through SGD which minimizes the average of local loss functions; see (1). The adversarial nodes are denoted in red color.  ... 
arXiv:2005.07866v1 fatcat:ixlbiuqipfcztchkwaewrej77q

Advances and Open Problems in Federated Learning [article]

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G.L. D'Oliveira, Hubert Eichner (+47 others)
2021 arXiv   pre-print
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service  ...  FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science  ...  The catastrophic potential of Byzantine attacks has spurred line of work on Byzantine-resilient aggregation mechanisms for distributed learning [75, 111, 342, 18, 497, 152] .  ... 
arXiv:1912.04977v3 fatcat:efkbqh4lwfacfeuxpe5pp7mk6a

BLOCKBENCH

Tien Tuan Anh Dinh, Ji Wang, Gang Chen, Rui Liu, Beng Chin Ooi, Kian-Lee Tan
2017 Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17  
Blockchain technologies are taking the world by storm. Public blockchains, such as Bitcoin and Ethereum, enable secure peer-to-peer applications like crypto-currency or smart contracts.  ...  It serves as a fair means of comparison for different platforms and enables deeper understanding of different system design choices.  ...  Gang Chen is supported by the National Natural Science Foundation of China (Grant No. 61472348).  ... 
doi:10.1145/3035918.3064033 dblp:conf/sigmod/DinhW0LOT17 fatcat:i32pmfvtpbhhto2ykzlbk3fo2e

Untangling Blockchain: A Data Processing View of Blockchain Systems [article]

Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, Ji Wang
2017 arXiv   pre-print
Blockchain technologies are gaining massive momentum in the last few years.  ...  Drawing from design principles of database systems, we discuss several research directions for bringing blockchain performance closer to the realm of databases.  ...  ACKNOWLEDGEMENTS This work is funded by the National Research Foundation, Prime Ministers Office, Singapore, under its Competitive Research Programme (CRP Award No. NRF-CRP8-2011-08).  ... 
arXiv:1708.05665v1 fatcat:tf3fuh2fxnbdjaxywuhc3qz6ra

BLOCKBENCH: A Framework for Analyzing Private Blockchains [article]

Tien Tuan Anh Dinh, Ji Wang, Gang Chen, Rui Liu, Beng Chin Ooi, Kian-Lee Tan
2017 arXiv   pre-print
Blockchain technologies are taking the world by storm. Public blockchains, such as Bitcoin and Ethereum, enable secure peer-to-peer applications like crypto-currency or smart contracts.  ...  It serves as a fair means of comparison for different platforms and enables deeper understanding of different system design choices.  ...  This work is funded by the National Research Foundation, Prime Minister's Office, Singapore, under its Competitive Research Programme (CRP Award No. NRF-CRP8-2011-08).  ... 
arXiv:1703.04057v1 fatcat:czc76zfsvfd3xhh5jmaob6veeq

Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis [article]

Tal Ben-Nun, Torsten Hoefler
2018 arXiv   pre-print
We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning.  ...  In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization.  ...  The multitude of hyper-parameters in SGD (e.g., learning rate, momentum, maximal staleness) and their adverse effect on the resulting accuracy hinders research efforts into new techniques in machine learning  ... 
arXiv:1802.09941v2 fatcat:ne2wiplln5eavjvjwf5to7nwsu

Untangling Blockchain: A Data Processing View of Blockchain Systems

Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, Ji Wang
2018 IEEE Transactions on Knowledge and Data Engineering  
Blockchain technologies are gaining massive momentum in the last few years.  ...  Drawing from design principles of database systems, we discuss several research directions for bringing blockchain performance closer to the realm of databases.  ...  ACKNOWLEDGMENTS This work is funded by the National Research Foundation, Prime Ministers Office, Singapore, under its Competitive Research Programme (CRP Award No. NRF-CRP8-2011-08).  ... 
doi:10.1109/tkde.2017.2781227 fatcat:7fedxsxy2jda7gz352iw2cyfzy
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