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Secure verifiable aggregation for blockchain-based federated averaging

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High-Confidence Computing

High-Confidence Computing 2022年第1期2卷 54-61页

作者： Saide Zhu Ruinian Li Zhipeng Cai Donghyun Kim Daehee Seo Wei Li Department of Computer Science Georgia State UniversityAtlantaUSA Department of Computer Science Bowling Green State UniversityBowling GreenOhioUSA Information Security Lab Sangmyung UniversitySeoulKorea

IoT devices’storage and computation capacities are constantly increasing in recent years,which brings critical challenges in data privacy protection.federated learning(FL)and blockchain technology are two popular tech-niques used in IoT data aggregation,where FL enables data training with privacy protection,and blockchain provides a decentralized architecture for data storage and mining.However,very few the state-of-the-art works consider the applicability of the combination of FL and blockchain.In this paper,we adopt the federated aver-aging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance.We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy.Finally,we propose and implement a non-interactive Public Verifiable Secret Sharing(PVSS)algorithm with Distributed Hash Table(DHT)that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users.At last,we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.

关键词： Blockchain federated learning Secret sharing Privacy

Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters:Future insights

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Artificial Intelligence in Medical Imaging 2022年第3期3卷 55-69页

作者： Prakash Nadkarni Suleman Adam Merchant College of Nursing University of IowaIowa CityIA 52242United States Department of Radiology LTM Medical College&LTM General HospitalMumbai 400022MaharashtraIndia

Much of the published literature in Radiology-related Artificial Intelligence(AI)focuses on single tasks,such as identifying the presence or absence or severity of specific lesions.Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image.Also,since a diagnosis is rarely achieved through an image alone,radiology AI must be able to employ diverse strategies that consider all available evidence,not just imaging information.Using key imaging and clinical signs will help improve their accuracy and utility tremendously.Employing strategies that consider all available evidence will be a formidable task;we believe that the combination of human and computer intelligence will be superior to either one alone.Further,unless an AI application is explainable,radiologists will not trust it to be either reliable or bias-free;we discuss some approaches aimed at providing better explanations,as well as regulatory concerns regarding explainability(“transparency”).Finally,we look at federated learning,which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models,and quantum computing,still prototypical but potentially revolutionary in its computing impact.

关键词： Medical imaging Artificial intelligence federated learning holistic approach Quantum computing Future insights

Robust peer-to-peer learning via secure multi-party computation

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Journal of Information and Intelligence

Journal of Information and Intelligence 2023年第4期1卷 341-351页

作者： Yongkang Luo Wenjian Luo Ruizhuo Zhang Hongwei Zhang Yuhui Shi Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen 518055China Peng Cheng Laboratory Shenzhen 518055China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

To solve the data island problem,federated learning(FL)provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation.Peer-to-peer(P2P)federated learning further improves the robustness of the system,in which there is no server and each client communicates directly with the other.For secure aggregation,secure multi-party computing(SMPC)protocols have been utilized in peer-to-peer manner.However,the ideal SMPC protocols could fail when some clients drop out.In this paper,we propose a robust peer-to-peer learning(RP2PL)algorithm via SMPC to resist clients dropping out.We improve the segmentbased SMPC protocol by adding a check and designing the generation method of random segments.In RP2PL,each client aggregates their models by the improved robust secure multi-part computation protocol when finishes the local training.Experimental results demonstrate that the RP2PL paradigm can mitigate clients dropping out with no significant degradation in performance.

关键词： federated learning Swarm learning Secure multi-party computation Peer-to-peer learning

A Collaborative Medical Diagnosis System Without Sharing Patient Data

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ZTE Communications 2022年第3期20卷 3-16页

作者： NAN Yucen FANG Minghao ZOU Xiaojing DOU Yutao Albert Y.ZOMAYA College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410003China Tongji Hospital Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430074China Center for Distributed and High Performance Computing University of SydneySydney 2008Australia

As more medical data become digitalized,machine learning is regarded as a promising tool for constructing medical decision support systems.Even with vast medical data volumes,machine learning is still not fully exploiting its potential because the data usually sits in data silos,and privacy and security regulations restrict their access and use.To address these issues,we built a secured and explainable machine learning framework,called explainable federated XGBoost(EXPERTS),which can share valuable information among different medical institutions to improve the learning results without sharing the patients’ data.It also reveals how the machine makes a decision through eigenvalues to offer a more insightful answer to medical professionals.To study the performance,we evaluate our approach by real-world datasets,and our approach outperforms the benchmark algorithms under both federated learning and non-federated learning frameworks.

关键词： explainable machine learning federated learning secured data analysis medical applications

MPHM:Model poisoning attacks on federal learning using historical information momentum

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Security and Safety

Security and Safety 2023年第4期2卷 6-18页

作者： Lei Shi Zhen Chen Yucheng Shi Lin Wei Yongcai Tao Mengyang He Qingxian Wang Yuan Zhou Yufei Gao School of Cyber Science and Engineering Zhengzhou UniversitySongShan LaboratoryZhengzhou 450000China College of Intelligence and Computing Tianjin University300072 TianjinChina School of Computer and Artificial Intelligence Zhengzhou University450000 ZhengzhouChina Zhengzhou Zhengda lnformation Technology Co. Ltd450001 ZhengzhouChina

federated learning(FL)development has grown increasingly strong with the increased emphasis on data for individuals and industry.federated learning allows individual participants to jointly train a global model without sharing local data,which significantly enhances data privacy.However,federated learning is vulnerable to poisoning attacks by malicious participants.Since federated learning does not have access to the participants’training process,i.e.,attackers can compromise the global model by uploading elaborate malicious local updates to the server under the guise of normal participants.Current model poisoning attacks usually add small perturbations to the local model after it is trained to craft harmful local updates and the attacker finds the appropriate perturbation size to bypass robust detection methods and corrupt the global model as much as possible.In contrast,we propose a novel model poisoning attack based on the momentum of history information(MPHM),that is,the attacker makes new malicious updates by dynamically crafting perturbations using the historical information in the local training,which will make the new malicious updates more effective and stealthy.Our attack aims to indiscriminately reduce the testing accuracy of the global model with minimal information.Experiments show that in the classical defense case,our attack can significantly corrupt the accuracy of the global model compared to other advanced poisoning attacks.

关键词： federated learning Poisoning attacks Security Privacy

federated selective aggregation for on-device knowledge amalgamation

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Chip 2023年第3期2卷 32-40页

作者： Donglin Xie Ruonan Yu Gongfan Fang Jiaqi Han Jie Song Zunlei Feng Li Sun Mingli Song Key Laboratory of Visual Perception Ministry of Education and MicrosoftZhe-jiang UniversityHangzhou 310000China School of Software Technology Zhejiang UniversityNingbo 315000China Ningbo Innovation Center Zhe-jiang UniversityNingbo 315000China

In the current work,we explored a new knowledge amalgama-tion problem,termed federated Selective Aggregation for on-device knowledge amalgamation(FedSA).FedSA aims to train an on-device student model for a new task with the help of several decentralized teachers whose pre-training tasks and data are different and agnos-tic.The motivation to investigate such a problem setup stems from a recent dilemma of model sharing.Due to privacy,security or in-tellectual property issues,the pre-trained models are,however,not able to be shared,and the resources of devices are usually limited.The proposed FedSA offers a solution to this dilemma and makes it one step further,again,the method can be employed on low-power and resource-limited devices.To this end,a dedicated strategy was proposed to handle the knowledge amalgamation.Specifically,the student-training process in the current work was driven by a novel saliency-based approach which adaptively selects teachers as the par-ticipants and integrated their representative capabilities into the stu-dent.To evaluate the effectiveness of FedSA,experiments on both single-task and multi-task settings were conducted.The experimental results demonstrate that FedSA could effectively amalgamate knowl-edge from decentralized models and achieve competitive performance to centralized baselines.

关键词： federated learning Knowledge amalgamation Model reusing

A review on medical brain image segmentation methods based on deep learning

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A review on medical brain image segmentation methods based o...