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Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
[article]
2023
arXiv
pre-print
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Federated Learning (FL) is a recent framework that has emerged as a promising approach for multiple learning agents to build an accurate and robust machine learning models without sharing raw data. ...
By allowing mobile handsets and devices to collaboratively learn a global model without explicit sharing of training data, FL exhibits high privacy and efficient spectrum utilization. ...
To solve the privacy issues in AR, the authors of [249] proposed a multi-user AR output strategy model using a hierarchical federated reinforcement learning method for generating and aggregating the ...
arXiv:2312.04688v1
fatcat:uwut2mfcrzdbdh5z5emhjg2iqa
APPFLChain: A Privacy Protection Distributed Artificial-Intelligence Architecture Based on Federated Learning and Consortium Blockchain
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2206.12790v1 fatcat:j7ax55zifveynlldyg3pnwggxm
To serve on this very purpose, we propose a new system architecture called APPFLChain, namely an integrated architecture of a Hyperledger Fabric-based blockchain and a federated-learning paradigm. ...
For numerical evaluation, we simulate a real-world scenario to illustrate the whole operational process of APPFLChain. ...
In recent years, federated learning (FL) has become a promising decentralized approach for training AI models [5] - [7] . ...
arXiv:2206.12790v2
fatcat:xq7zpcodk5c6zf4icsmxztzspa
A Survey on Decentralized Federated Learning
[article]
2023
arXiv
pre-print
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In recent years, federated learning (FL) has become a very popular paradigm for training distributed, large-scale, and privacy-preserving machine learning (ML) systems. ...
To mitigate such exposure, decentralized FL solutions have emerged where all FL clients cooperate and communicate without a central server. ...
CONCLUSION In recent years, federated learning (FL) has gained popularity as a paradigm for training distributed, large-scale, and privacy-preserving machine learning systems. ...
arXiv:2308.04604v1
fatcat:husyihgjhzarxjnemvy3m7bqv4
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications
[article]
2023
arXiv
pre-print
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In the realm of machine learning (ML) systems featuring client-host connections, the enhancement of privacy security can be effectively achieved through federated learning (FL) as a secure distributed ...
Through this mechanism, it guarantees the streamlined processing and data storage requirements of both centralized and decentralized systems, with an emphasis on scalability, privacy considerations, and ...
The challenge in decentralized learning is effectively coordinating decentralized learning while preserving data privacy and learning security across the board. ...
arXiv:2310.05269v2
fatcat:pe5ho54xfbfdxfkarwklodt6ha
Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis
2021
IEEE Access
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FEDERATED LEARNING WITH BLOCKCHAIN Blockchain is a decentralized, provenance-preserving, immutable ledger technique. ...
Through a formal recovery error bound, the proposed privacy-preserving tensor completion method is proven that can provide a privacy guarantee with high accuracy.
III. ...
He was a recipient of the 2021 IEEE TEM BEST PAPER AWARD. His current H-index is 21, i10-index is 35 ...
doi:10.1109/access.2021.3118642
fatcat:222fgsvt3nh6zcgm5qt4kxe7c4
Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges
2022
Algorithms
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These systems could be considered a source of privacy-aware risks. ...
Another benefit of the usage of federated learning for intrusion detection is its ability to support collaboration between entities that could not share their dataset for confidential or other reasons. ...
Privacy-preserving mechanism. Hyperledger fabric (blockchain). ML method. ...
doi:10.3390/a15070247
fatcat:fr7wbjns2fhllprc534q2s46pi
Vulnerabilities in Federated Learning
2021
IEEE Access
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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 ...
While FL has recently emerged as a promising solution to preserve users' privacy, this new paradigm's potential security implications may hinder its widespread adoption. ...
FEDERATED MULTI-TASK LEARNING Federated multi-task learning [116] - [119] handles statistical and system challenges of FL like high communication costs, stragglers, and fault tolerance. ...
doi:10.1109/access.2021.3075203
doaj:5e62c955db514036939a1c65011f46b8
fatcat:viv7tij6cffnlev4l52wggkxfe
Privacy-Preserving Online Content Moderation: A Federated Learning Use Case
[article]
2022
arXiv
pre-print
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In this paper, we propose a privacy-preserving FL framework for online content moderation that incorporates Differential Privacy (DP). ...
Federated Learning (FL) is an ML paradigm where the training is performed locally on the users' devices. ...
For this purpose, we propose and evaluate a privacy-preserving Federated Learning (FL) framework for training text classifiers able to detect harmful content. ...
arXiv:2209.11843v1
fatcat:6th7m5zuwzavfnooi6owwt32ii
Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence
[article]
2021
arXiv
pre-print
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2022 accepted arXiv:2105.01798v2 fatcat:nqyjlsyvxncybnc23uccj7zaqy
Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life ...
We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. ...
ACKNOWLEDGMENT This work was made possible by NPRP grant NPRP12S-0305-190231 and NPRP13S-0205-200265 from the Qatar National Research Fund (a member of Qatar Foundation). ...
arXiv:2105.01798v1
fatcat:4tnq2wjw4bcqdfvhnoij55s2rm
PPBFL: A Privacy Protected Blockchain-based Federated Learning Model
[article]
2024
arXiv
pre-print
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With the rapid development of machine learning and a growing concern for data privacy, federated learning has become a focal point of attention. ...
Within the blockchain, we introduce a Proof of Training Work (PoTW) consensus algorithm tailored for federated learning, aiming to incentive training nodes. ...
However, more effective methods for preserving model parameter privacy and encouraging the engagement of training clients in federated learning are still areas that require further research. ...
arXiv:2401.01204v2
fatcat:nrzwprogdbhdbefsvtorfsnv5u
Federated Learning on Non-IID Data: A Survey
[article]
2021
arXiv
pre-print
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Federated learning is an emerging distributed machine learning framework for privacy preservation. ...
In this survey, we pro-vide a detailed analysis of the influence of Non-IID data on both parametric and non-parametric machine learning models in both horizontal and vertical federated learning. ...
For example, MOCHA, a representative framework for federated multi-task learning (FMTL), firstly considers issues of communication cost, stragglers and fault tolerance for FL [122] . ...
arXiv:2106.06843v1
fatcat:qsfetsjmxrb6zhhuesgxcjuxj4
Privacy–Preserving Online Content Moderation: A Federated Learning Use Case
2023
Companion Proceedings of the ACM Web Conference 2023
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In this paper, we propose a framework for developing content moderation tools in a privacy-preserving manner where sensitive information stays on the users' device. ...
For this purpose, we apply Diferentially Private Federated Learning (DP-FL), where the training of ML models is performed locally on the users' devices, and only the model updates are shared with a central ...
In this paper, we propose a privacy-preserving Federated Learning (FL) framework for detecting harmful online content. ...
doi:10.1145/3543873.3587604
fatcat:tdc6grwcjjc45ln6lguboap7wm
The Dichotomy of Cloud and IoT: Cloud-Assisted IoT From a Security Perspective
[article]
2022
arXiv
pre-print
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We develop a layered architecture for SCAIoT. Furthermore, we take a look at what the future may hold for SCAIoT with a focus on the role of Artificial Intelligence(AI). ...
In recent years, the existence of a significant cross-impact between Cloud computing and Internet of Things (IoT) has lead to a dichotomy that gives raise to Cloud-Assisted IoT (CAIoT) and IoT-Based Cloud ...
The authors of [205] , propose an adaptive dropout deep computation model (ADDCM) with crowdsourcing to the cloud for industrial IoT big data feature learning. ...
arXiv:2207.01590v2
fatcat:2kznp4sj7resznpiylio3wfrsa
Digital Privacy Under Attack: Challenges and Enablers
[article]
2023
arXiv
pre-print
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We develop a study to fill this gap by assessing the resilience of privacy-preserving methods to various attacks and conducting a comprehensive review of countermeasures from a broader perspective. ...
Therefore, a holistic survey to compare the discovered techniques on attacks over privacy preservation and their mitigation schemes is essential in the literature. ...
Federated learning (FL) is also regarded as the latest breakthrough within the scope of privacy-preserving machine learning research works, in which models are trained in a decentralized manner by independent ...
arXiv:2302.09258v1
fatcat:mvmqjtt7azhm3bvjq37uj5yjeu
2021 Index IEEE Transactions on Intelligent Transportation Systems Vol. 22
2021
IEEE transactions on intelligent transportation systems (Print)
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TITS Aug. 2021 5275-5282 A Decentralized Location Privacy-Preserving Spatial Crowdsourcing for Internet of Vehicles. ...
Data privacy A Decentralized Location Privacy-Preserving Spatial Crowdsourcing for Internet of Vehicles.Zhang, J., +, TITS April 2021 2299-2313 A Deep Learning-Based Blockchain Mechanism for Secure Internet ...
doi:10.1109/tits.2021.3139738
fatcat:p2mkawtrsbaepj4zk24xhyl2oa
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