Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence

Meng, Hao; Li, Hongwei; Luo, Xizhao; Xu, Guowen; Yang, Haomiao; Liu, Sen

doi:10.1109/tii.2019.2945367

Cited by 449 publications

(197 citation statements)

References 36 publications

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“…Decentralized learning schemes can be used to address the challenges of DL frameworks. In particular, the technique of FL can be employed to alleviate the challenges of DL frameworks [43], [125], [138], [140].…”

Section: ) Dl-based Edge Cachingmentioning

confidence: 99%

“…FL is achieved by exchanging only model parameters learned locally at each UE [85], [141], [143], [144]. Furthermore, FL presents unique and attractive features that facilitate collaborative training of local learning models without compromising the privacy of training data [9], [24], [41], [140]. In general, FL presents an efficient training pattern which involves three main phases as demonstrated in Fig.…”

Section: ) Fl-based Edge Cachingmentioning

confidence: 99%

“…Considering the distributed nature of training across the numerous devices, it becomes possible to have free ridership [37]. Concerning the privacy preservation property of FL, it was shown in [37], [43], [140], [144] that some of the existing FL frameworks may still be susceptible to various privacy attacks.…”

Section: ) Fl-based Edge Cachingmentioning

confidence: 99%

“…Consequently, future research should investigate the operational features of the emerging personalized federated learning approaches such as federated transfer learning, federated meta learning, federated multi-task learning, and federated distillation. Moreover, more research is needed to improve the performance of FL frameworks as it was shown in [39], [43], [140] that although the frameworks are effective at reducing latency, they may be less effective at guaranteeing the required data privacy. Several issues regarding FL techniques were discussed in the recent work of [37], [39], [40], [90], [138], [142], [144], [146], [147], [152]- [154].…”

Section: Edge Caching and In-network Cachingmentioning

confidence: 99%

See 3 more Smart Citations

A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

Mutalemwa

Shin

2020

IEEE Access

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Section: ) Dl-based Edge Cachingmentioning

confidence: 99%

Section: ) Fl-based Edge Cachingmentioning

confidence: 99%

Section: ) Fl-based Edge Cachingmentioning

confidence: 99%

Section: Edge Caching and In-network Cachingmentioning

confidence: 99%

See 2 more Smart Citations

A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

Mutalemwa

Shin

2020

IEEE Access

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“…For example, a malicious user in a network of shared parameters can access the personal images of users from surveillance systems through various attacks. In a different context, several adversaries can similarly violate the emergency responses of autonomous vehicles or change the health records of several patients from their wearable devices [11]. These threats not only result in serious privacy leakages but also bring in unpredictable life losses.…”

mentioning

confidence: 99%

FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning

2020

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Artificial Intelligence (AI) has been applied to solve various challenges of real-world problems in recent years. However, the emergence of new AI technologies has brought several problems, especially with regard to communication efficiency, security threats and privacy violations. Towards this end, Federated Learning (FL) has received widespread attention due to its ability to facilitate the collaborative training of local learning models without compromising the privacy of data. However, recent studies have shown that FL still consumes considerable amounts of communication resources. These communication resources are vital for updating the learning models. In addition, the privacy of data could still be compromised once sharing the parameters of the local learning models in order to update the global model. Towards this end, we propose a new approach, namely, Federated Optimisation (FedOpt) in order to promote communication efficiency and privacy preservation in FL. In order to implement FedOpt, we design a novel compression algorithm, namely, Sparse Compression Algorithm (SCA) for efficient communication, and then integrate the additively homomorphic encryption with differential privacy to prevent data from being leaked. Thus, the proposed FedOpt smoothly trade-offs communication efficiency and privacy preservation in order to adopt the learning task. The experimental results demonstrate that FedOpt outperforms the state-of-the-art FL approaches. In particular, we consider three different evaluation criteria; model accuracy, communication efficiency and computation overhead. Then, we compare the proposed FedOpt with the baseline configurations and the state-of-the-art approaches, i.e., Federated Averaging (FedAvg) and the paillier-encryption based privacy-preserving deep learning (PPDL) on all these three evaluation criteria. The experimental results show that FedOpt is able to converge within fewer training epochs and a smaller privacy budget.

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VFLF: A verifiable federated learning framework against malicious aggregators in Industrial Internet of Things

Zhou

Tian

Peng

et al. 2022

Concurrency and Computation

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As an important approach to overcome data silos and privacy concerns in deep learning, federated learning, which can jointly train the global model and keep data local, has shown remarkable performance in a range of industrial applications. However, federated learning still suffers from the problem that shared gradients may be subject to tampering, inference functions, and falsification. To address this issue, we propose a verifiable federated learning framework to deal with malicious aggregators. Initially, we propose a reputation calculation mechanism to solve the problem of selecting a reliable aggregator based on a multiweight subjective logic model. Furthermore, we design a verifiable federated learning scheme to ensure data confidentiality, integrity, and verifiability, as well as support the client's dynamic withdrawal. Security analyses indicate that our framework is secure against malicious adversaries. Furthermore, experimental results on real datasets show that our verifiable federated learning has high accuracy and feasible efficiency.

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Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence

Cited by 449 publications

References 36 publications

A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning

VFLF: A verifiable federated learning framework against malicious aggregators in Industrial Internet of Things

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