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Enhancing the Privacy of Federated Learning with Sketching
[article]
2019
arXiv
pre-print
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We evaluate the feasibility of sketching-based federated learning with a prototype on three representative learning models. ...
Existing efforts that aim to improve the privacy of federated learning make compromises in one or more of the following key areas: performance (particularly communication cost), accuracy, or privacy. ...
., Intel SGX [18] and ARM Trusted Zone [7] ) to enhance the trustworthy and privacy of federated learning [34] . ...
arXiv:1911.01812v1
fatcat:mquqgd2ykjepdidtf5bx4rkkpq
FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework
[article]
2020
arXiv
pre-print
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The framework pre-learns the pattern of the feature to directly judge the usefulness of the transformation on a feature. ...
We made experiments on datasets of both open-sourced and real-world thus validating the comparable effectiveness of FLFE to evaluation-based approaches, along with the far more superior efficacy. ...
Federated Learning Federated learning (FL) [20] is a scenario where multiple clients collaboratively train a machine learning (ML) model with the help of a central server. ...
arXiv:2009.02557v1
fatcat:hfhi53vjxzejpi3c2maoo45s2u
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches
[article]
2019
arXiv
pre-print
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In this work, we argue that a natural connection exists between methods for communication reduction and privacy preservation in the context of distributed machine learning. ...
Using these derived privacy guarantees, we propose a novel sketch-based framework (DiffSketch) for distributed learning, where we compress the transmitted messages via sketches to simultaneously achieve ...
our vision appeared in [33] , where we suggested the promise of using sketching to enhance privacy in federated learning. ...
arXiv:1911.00972v2
fatcat:hmsabj5wyjem5ikmgtb5yyeso4
Revolutionizing comic coloring: Federated learning-based neural network for efficiency and privacy
2024
Applied and Computational Engineering
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Experimental results demonstrate that this approach has been effective in comic coloring, demonstrating the feasibility of applying federated learning to the task of comic coloring. ...
This study proposes an innovative approach that applies federated learning to comic coloring to improve efficiency and address privacy issues. ...
To enhance the efficiency of comic creation, attempts have been made to integrate deep learning with the coloring process [1, 2] . ...
doi:10.54254/2755-2721/51/20241159
fatcat:q3in3qf7jfe3bclzhpxtdngvbm
AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI
[article]
2021
arXiv
pre-print
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Our results indicate that with FML, forecasting accuracy is increased while preserving the data privacy of the end-users. ...
This paper addresses this challenge and proposes a privacy-preserving federated learning framework for ML applications in the AMI. ...
We presented a generalized federated learning based paradigm for developing privacy-preserving ML applications (AMI-FML) in the smart grid. ...
arXiv:2109.05666v2
fatcat:3jciof2rmbfdvbkhxrkmo23ifi
An Introduction to Federated Computation
2022
Proceedings of the 2022 International Conference on Management of Data
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The most prominent application of federated computation is in training machine learning models (federated learning), but many additional applications are emerging, more broadly relevant to data management ...
Federated Computation is an emerging area that seeks to provide stronger privacy for user data, by performing large scale, distributed computations where the data remains in the hands of users. ...
Akash Bharadwaj is a technical lead (research) at Meta in the USA, working on applications of federated analytics and other privacy enhancing technologies. ...
doi:10.1145/3514221.3522561
fatcat:og5gh5asqrgfllxa7cuex6y6gi
Evolutionary tree-based quasi identifier and federated gradient privacy preservations over big healthcare data
2022
International Journal of Power Electronics and Drive Systems (IJPEDS)
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Next with the learnt quasi-identifiers, privacy preservation of data item is made by applying federated gradient arbitrary privacy preservation learning model. ...
This model attains optimal balance between privacy and accuracy. In the federated gradient privacy preservation learning model, we evaluate the determinant of each attribute to the outputs. ...
Privacy-preserving federated machine learning process is specifically designed on the concept of Differential Privacy. ...
doi:10.11591/ijece.v12i1.pp903-913
fatcat:gqesekgqgjbjrhcsl2fxca7dui
Rebirth of Distributed AI—A Review of eHealth Research
2021
Sensors
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Privacy of the data and training time have been major roadblocks to achieving the specific goals of each application domain. ...
Federated learning, with its distributed and local training approach, stands out as a potential solution to these challenges. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21154999
fatcat:msnxvfkgwvatxhziel5x6d4l2a
A Security-Friendly Privacy Solution for Federated Learning
2022
Zenodo
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Federated learning is a privacy-aware collaborative machine learning method, but it needs other privacy enhancing technologies to prevent data leakage from local model updates. ...
Solutions that satisfy both privacy and security at the same time are needed for federated learning. ...
Acknowledgments This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) through the 1515 Frontier Research and Development Laboratories Support Program under Project ...
doi:10.5281/zenodo.7359884
fatcat:id5kp5p7nvhbvj4vpx27qol72a
Collaborative Semantic Aggregation and Calibration for Separated Domain Generalization
[article]
2022
arXiv
pre-print
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A dilemma is thus raised between the generalization learning with shared multi-source data and the privacy protection of real-world sensitive data. ...
To address the semantic dislocation problem caused by domain shift, we further design cross-layer semantic calibration with an elaborate attention mechanism to align each semantic level and enhance domain ...
Federated Learning As an active research field towards modern privacy protection, federated learning [13] , [33] , [38] , [56] , [57] makes local clients jointly train a model with a central server ...
arXiv:2110.06736v3
fatcat:tmmfsgritjcjbjg62pp4vw35oi
System Optimization in Synchronous Federated Training: A Survey
[article]
2021
arXiv
pre-print
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The unprecedented demand for collaborative machine learning in a privacy-preserving manner gives rise to a novel machine learning paradigm called federated learning (FL). ...
Given a sufficient level of privacy guarantees, the practicality of an FL system mainly depends on its time-to-accuracy performance during the training process. ...
called federated learning (FL) [6] . ...
arXiv:2109.03999v2
fatcat:oxmq44iuo5eexbjtq7xdj3quq4
Federated Learning: Challenges, Methods, and Future Directions
[article]
2019
arXiv
pre-print
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In this article, we discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant ...
, and privacy-preserving data analysis. ...
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA, the National Science Foundation, or any ...
arXiv:1908.07873v1
fatcat:pcztnmhquvd65es6wdz34igbhi
ESMFL: Efficient and Secure Models for Federated Learning
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2009.01867v1 fatcat:72km6q6zcfhkzhnbjkhwmtnucy
To address these problems, we propose a privacy-preserving method for the federated learning distributed system, operated on Intel Software Guard Extensions, a set of instructions that increase the security ...
However, massive data collection required for deep neural network reveals the potential privacy issues and also consumes large mounts of communication bandwidth. ...
Our federated learning system ensure the data privacy without adding noise. ...
arXiv:2009.01867v2
fatcat:4x7catj5c5cj5dgtlb3rg3ufry
NegDL: Privacy-Preserving Deep Learning Based on Negative Database
[article]
2022
arXiv
pre-print
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Specifically, private data are first converted to NDB as the input of deep learning models by a generation algorithm called QK-hidden algorithm, and then the sketches of NDB are extracted for training ...
We demonstrate that the computational complexity of NegDL is the same as the original deep learning model without privacy protection. ...
[10] proposed a more secure privacy-preserving federated learning model. ...
arXiv:2103.05854v5
fatcat:rcilsoqwfnhgjpadhwvd3ewjiy
Page 58 of Newsletter on Intellectual Freedom Vol. 29, Issue 3
[page]
1980
Newsletter on Intellectual Freedom
Preserved Fulltext
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San Francisco, California
Ruling on a suit filed against the Federal Bureau of Investigation by Judith Exner, who was romantically linked with the late President John F. Kennedy, the U.S. ...
Stein, a former lawyer for the Immigration and Naturalization Service, filed a $6 million suit against the government after learning that the FBI had ‘‘passed’’ information about him in order to enhance ...
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