PEFL: A Privacy-Enhanced Federated Learning Scheme for Big Data Analytics

Zhang, Jiale; Chen, Bing; Yu, Shui; Deng, Hai

doi:10.1109/globecom38437.2019.9014272

Cited by 59 publications

(43 citation statements)

References 15 publications

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“…In order to protect the privacy of the gradients from an untrusted server, Zhang et al [29] propose a privacy-enhanced FL scheme based on the additively homomorphic cryptosystem which enables applying functions on encrypted data without revealing the values of the data. A distributed selective SGD method is employed to achieve distributed encryptions and reduce the communication costs.…”

Section: B Privacy and Security Of Flmentioning

confidence: 99%

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Yoshinaga

et al. 2020

IEEE Open J. Comput. Soc.

268

106

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Section: B Privacy and Security Of Flmentioning

confidence: 99%

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Yoshinaga

et al. 2020

IEEE Open J. Comput. Soc.

268

106

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“…Therefore, it is widely applied to parameter aggregation on servers. Zhang et al [11] proposed PEFL, a distributed machine learning privacy-preserving scheme based on homomorphic encryption. This scheme can perform computation directly using ciphertext to achieve secure aggregation.…”

Section: Cryptography-based Secure Aggregation Algorithm In Federated Learningmentioning

confidence: 99%

“…To prevent gradient leakage, users can rely on homomorphic encryption, function encryption, and other cryptographic methods to encrypt local models, and service providers do not have direct access to the plaintext of individual gradients. Zhang et al [11] proposed a distributed selective stochastic gradient descent algorithm combined with Paillier homomorphic encryption. In this scheme, a trusted third party (TTP) assigns keys to users and the server, and the server uses Paillier additive homomorphism to achieve secure gradient aggregation.…”

Section: Introductionmentioning

confidence: 99%

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

Habib¹,

Kiani²,

Butt³

et al. 2022

Computers, Materials &Amp; Continua

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Federated learning is a distributed learning framework which trains global models by passing model parameters instead of raw data. However, the training mechanism for passing model parameters is still threatened by gradient inversion, inference attacks, etc. With a lightweight encryption overhead, function encryption is a viable secure aggregation technique in federation learning, which is often used in combination with differential privacy. The function encryption in federal learning still has the following problems: a) Traditional function encryption usually requires a trust third party (TTP) to assign the keys. If a TTP colludes with a server, the security aggregation mechanism can be compromised. b) When using differential privacy in combination with function encryption, the evaluation metrics of incentive mechanisms in the traditional federal learning become invisible. In this paper, we propose a hybrid privacy-preserving scheme for federated learning, called Fed-DFE. Specifically, we present a decentralized multi-client function encryption algorithm. It replaces the TTP in traditional function encryption with an interactive key generation algorithm, avoiding the problem of collusion. Then, an embedded incentive mechanism is designed for function encryption. It models the real parameters in federated learning and finds a balance between privacy preservation and model accuracy. Subsequently, we implemented a prototype of Fed-DFE and evaluated the performance of decentralized function encryption algorithm. The experimental results demonstrate the effectiveness and efficiency of our scheme.

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“…Yang et al [ 19 ] depicted the necessary abstraction, architecture, and various applications of federated learning from an overview perspective. FL technique earned a remarkable reputation in many pragmatic fields such as visual object detection [ 20 ], health records [ 21 ], big data analysis [ 22 ], control policies [ 23 ], and medical text extraction [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

FedMed: A Federated Learning Framework for Language Modeling

Liang

Wang

2020

Sensors

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Federated learning (FL) is a privacy-preserving technique for training a vast amount of decentralized data and making inferences on mobile devices. As a typical language modeling problem, mobile keyboard prediction aims at suggesting a probable next word or phrase and facilitating the human-machine interaction in a virtual keyboard of the smartphone or laptop. Mobile keyboard prediction with FL hopes to satisfy the growing demand that high-level data privacy be preserved in artificial intelligence applications even with the distributed models training. However, there are two major problems in the federated optimization for the prediction: (1) aggregating model parameters on the server-side and (2) reducing communication costs caused by model weights collection. To address the above issues, traditional FL methods simply use averaging aggregation or ignore communication costs. We propose a novel Federated Mediation (FedMed) framework with the adaptive aggregation, mediation incentive scheme, and topK strategy to address the model aggregation and communication costs. The performance is evaluated in terms of perplexity and communication rounds. Experiments are conducted on three datasets (i.e., Penn Treebank, WikiText-2, and Yelp) and the results demonstrate that our FedMed framework achieves robust performance and outperforms baseline approaches.

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PEFL: A Privacy-Enhanced Federated Learning Scheme for Big Data Analytics

Cited by 59 publications

References 15 publications

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Fed-DFE: A Decentralized Function Encryption-Based Privacy-Preserving Scheme for Federated Learning

FedMed: A Federated Learning Framework for Language Modeling

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