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

Zhixiong Yang, Arpita Gang, Waheed U. Bajwa
<span title="2020-02-08">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this article, we provide an overview of some of the most recent developments in this area under the threat model of Byzantine attacks.  ...  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.  ...  ACKNOWLEDGEMENTS The authors gratefully acknowledge the support of the NSF (CCF-1453073, CCF-1907658), the ARO (W911NF-17-1-0546), and the DARPA Lagrange Program (ONR/SPAWAR contract N660011824020).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1908.08649v2">arXiv:1908.08649v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/de356dvwinfv5g5njo64qmzpvi">fatcat:de356dvwinfv5g5njo64qmzpvi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321143519/https://arxiv.org/pdf/1908.08649v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1908.08649v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

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

Djamila Bouhata, Hamouma Moumen
<span title="2022-05-05">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Byzantine Fault Tolerance (BFT) is among the most challenging problems in Distributed Machine Learning (DML).  ...  We offer an illustrative description of techniques used in BFT in DML, with a proposed classification of BFTs approaches in the context of their basic techniques.  ...  They focused on the latest advances in Byzantine-resilient inference and learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.02572v1">arXiv:2205.02572v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h2hkcgz3w5cvrnro6whl2rpvby">fatcat:h2hkcgz3w5cvrnro6whl2rpvby</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220509223512/https://arxiv.org/pdf/2205.02572v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/85/e6/85e6587a4bf74a8dd18500dee26060d5a6155634.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.02572v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey [article]

Shangwei Guo, Xu Zhang, Fei Yang, Tianwei Zhang, Yan Gan, Tao Xiang, Yang Liu
<span title="2021-12-19">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our survey first provides the system overview of collaborative learning, followed by a brief introduction of integrity and privacy threats.  ...  With the rapid demand of data and computational resources in deep learning systems, a growing number of algorithms to utilize collaborative machine learning techniques, for example, federated learning,  ...  Dimensions of Parallelism Machine learning is growing rapidly in the recent decade due to the growth of the sizes of models and datasets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.10183v1">arXiv:2112.10183v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ujfz4a5mdrhsbk4kiqoqo2snfe">fatcat:ujfz4a5mdrhsbk4kiqoqo2snfe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211228051834/https://arxiv.org/pdf/2112.10183v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2f/05/2f0593543cd77bdc37943effab25cb7090bbc407.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.10183v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Vulnerabilities in Federated Learning

Nader Bouacida, Prasant Mohapatra
<span title="">2021</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Therefore, it is crucial to raise awareness of the consequences resulting from the new threats to FL systems. To date, the security of traditional machine learning systems has been widely examined.  ...  A new decentralized training paradigm, known as Federated Learning (FL), enables multiple clients located at different geographical locations to learn a machine learning model collaboratively without sharing  ...  FL also considers the users of the final trained model as potential adversaries. The deployed model should remain resilient in inference time against adversaries who are users of the service. III.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3075203">doi:10.1109/access.2021.3075203</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/5e62c955db514036939a1c65011f46b8">doaj:5e62c955db514036939a1c65011f46b8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/viv7tij6cffnlev4l52wggkxfe">fatcat:viv7tij6cffnlev4l52wggkxfe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210613071126/https://ieeexplore.ieee.org/ielx7/6287639/9312710/09411833.pdf?tp=&amp;arnumber=9411833&amp;isnumber=9312710&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4e/b8/4eb8c7023c57f76d5e2401c033e7e2769be03a79.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3075203"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Privacy and Robustness in Federated Learning: Attacks and Defenses [article]

Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu
<span title="2022-01-19">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy  ...  Recently, federated learning (FL) has emerged as an alternative solution and continue to thrive in this new reality.  ...  To fill in this gap, in this paper, we have conducted an extensive survey on the recent advances in privacy and robustness threats to FL and their defenses.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.06337v3">arXiv:2012.06337v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f5aflxnsdrdcdf4kvoa6yzseqq">fatcat:f5aflxnsdrdcdf4kvoa6yzseqq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220124182618/https://arxiv.org/pdf/2012.06337v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/84/48/8448010d9adad18bf36070c012770a10ecb21c76.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.06337v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

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

Jiacheng Liang, Songze Li, Bochuan Cao, Wensi Jiang, Chaoyang He
<span title="2021-11-15">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution.  ...  We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications.  ...  Current strategies to defend Byzantine clients mainly follow distributed machine learning protocols designed under adversarial settings (Blanchard et al. 2017a; Chen, Su, and Xu 2017; Yin et al. 2018;  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.05252v4">arXiv:2107.05252v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u2uaa4fbrvdb3jqbipttcmtvq4">fatcat:u2uaa4fbrvdb3jqbipttcmtvq4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211130212832/https://arxiv.org/pdf/2107.05252v4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/81/80/81802e5cbc5c80f89a17156958836ac9f0743d68.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.05252v4" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness [article]

Yuwei Sun, Hideya Ochiai, Hiroshi Esaki
<span title="2021-12-06">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Decentralized deep learning (DDL) such as federated learning and swarm learning as a promising solution to privacy-preserving data processing for millions of smart edge devices, leverages distributed computing  ...  Furthermore, we offer a comprehensive overview of the current state-of-the-art in the field by outlining the challenges of DDL and the most relevant solutions from novel perspectives of communication efficiency  ...  Han, “Federated “Machine learning with adversaries: Byzantine tolerant gradient reinforcement learning for training control policies on multiple descent,” in Advances  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.03980v4">arXiv:2108.03980v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3chrjozkxrdzljthkjzlagg6uy">fatcat:3chrjozkxrdzljthkjzlagg6uy</a> </span>
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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)
<span title="2021-03-09">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.  ...  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  ...  Acknowledgments The authors would like to thank Alex Ingerman and David Petrou for their useful suggestions and insightful comments during the review process.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.04977v3">arXiv:1912.04977v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/efkbqh4lwfacfeuxpe5pp7mk6a">fatcat:efkbqh4lwfacfeuxpe5pp7mk6a</a> </span>
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2020 SP Year End Indexes

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/txj4mzfpbne4hhwgo2ku5cycbq" style="color: black;">IEEE Signal Processing Magazine</a> </i> &nbsp;
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  Sensing: From Research to Clinical Practice With Deep Neural Adversary-Resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/msp.2020.3037982">doi:10.1109/msp.2020.3037982</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ny2cx6ix45cfhcqgpvituyiri4">fatcat:ny2cx6ix45cfhcqgpvituyiri4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201203210655/https://ieeexplore.ieee.org/ielx7/79/9244169/09273288.pdf?tp=&amp;arnumber=9273288&amp;isnumber=9244169&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/06/29/06294ac8dc849dc3ce5c5506ffab0c579d13aa1c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/msp.2020.3037982"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Separation of Powers in Federated Learning [article]

Pau-Chen Cheng, Kevin Eykholt, Zhongshu Gu, Hani Jamjoom, K. R. Jayaram, Enriquillo Valdez, Ashish Verma
<span title="2021-05-19">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Thus, each aggregator only has a fragmentary and shuffled view of model updates and is oblivious to the model architecture.  ...  Based on the unique computational properties of model-fusion algorithms, all exchanged model updates in TRUDA are disassembled at the parameter-granularity and re-stitched to random partitions designated  ...  Recent research [12, 39, 62, 63, 67] has demonstrated the feasibility and ease of inferring private attributes and reconstructing large fractions of training data by exploiting model updates, thereby  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.09400v1">arXiv:2105.09400v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ewbxs33eijfx5fsbmnsi62odl4">fatcat:ewbxs33eijfx5fsbmnsi62odl4</a> </span>
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Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning [article]

Chuan Ma, Jun Li, Kang Wei, Bo Liu, Ming Ding, Long Yuan, Zhu Han, H. Vincent Poor
<span title="2022-03-01">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
an ML process, i.e.: i) the level of preprocessed data, ii) the level of learning models, iii) the level of extracted knowledge and, iv) the level of intermediate results.  ...  We explore and analyze the potential of threats for each information exchange level based on an overview of the current state-of-the-art attack mechanisms, and then discuss the possible defense methods  ...  The work in [166] has aimed to develop secure, resilient and distributed machine learning algorithms under adversarial environments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.09027v2">arXiv:2202.09027v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hlu7bopcjrc6zjn2pct57utufy">fatcat:hlu7bopcjrc6zjn2pct57utufy</a> </span>
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Security and Privacy in IoT Using Machine Learning and Blockchain: Threats Countermeasures [article]

Nazar Waheed, Xiangjian He, Muhammad Ikram, Muhammad Usman, Saad Sajid Hashmi, Muhammad Usman
<span title="2020-08-06">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers.  ...  Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based  ...  The computational resources of an IoT device are limited, so the capabilities of dealing with advanced threats are degraded.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.03488v4">arXiv:2002.03488v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cxavellepncexgkfcs5phdj53u">fatcat:cxavellepncexgkfcs5phdj53u</a> </span>
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Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions [article]

Thippa Reddy Gadekallu, Quoc-Viet Pham, Thien Huynh-The, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Madhusanka Liyanage
<span title="2021-10-17">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this article, we present a survey on the use of FL for big data services and applications, aiming to provide general readers with an overview of FL, big data, and the motivations behind the use of FL  ...  Big data has remarkably evolved over the last few years to realize an enormous volume of data generated from newly emerging services and applications and a massive number of Internet-of-Things (IoT) devices  ...  Acknowledgement We acknowledge the authors (Dinh, Fang, Pubudu) for the contribution of our (blockchain -big data) development.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.04160v2">arXiv:2110.04160v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3y2kmamdbrfmrjdxv3zh47yphu">fatcat:3y2kmamdbrfmrjdxv3zh47yphu</a> </span>
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PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy [article]

Xiaolan Gu, Ming Li, Li Xiong
<span title="2021-10-22">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Federated Learning (FL) allows multiple participating clients to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates.  ...  ., secret sharing), noise is added to the model updates by the honest-but-curious server(s) (instead of each client) without revealing clients' inputs, which achieves the benefit of centralized DP in terms  ...  The threat models of them are different: Record Inference Attacks.  ... 
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When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges [article]

Qun Wang, Haijian Sun, Rose Qingyang Hu, Arupjyoti Bhuyan
<span title="2022-01-12">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms.  ...  Machine learning (ML) based methods have frequently been proposed to address those issues.  ...  By formulating a min-max game, an adversarial training algorithm was designed to minimize the prediction loss of the model and the maximum gain of the inference attacks.  ... 
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