Privacy Preserved Decentralized Deep Learning: A Blockchain Based Solution for Secure AI-Driven Enterprise

Fadaeddini, Amin; Majidi, Babak; Eshghi, Mohammad

doi:10.1007/978-3-030-33495-6_3

Cited by 11 publications

(14 citation statements)

References 7 publications

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“…Blockchains are open access where participants can deploy contracts pseudoanonymously 2) Private Blockchains do not allow access for clients outside the private network and require an entity that controls who is permitted to participate 3) Permissioned blockchains are private blockchains with a decentralized committee which controls the onboarding process Note that the FLFs that utilize open-source public blockchains such as Ethereum [32], [33], [35], [39], [41], [43], [50], [54], [56], [57], [64], Stellar [51] and EOS [60] were not deployed on the respective public blockchain in the experiments due to the enormous costs this would incur.…”

Section: ) Publicmentioning

confidence: 99%

“…Another way blockchain coordinates the FL process is by enabling the infrastructure for trust-less voting atop of the blockchain. Voting on the next leader (aggregator) [51], [52] or on each others contributions [58], [59], [68] further democratizes the process. Beyond explicit coordination operations like voting or leader selection, the implicit function of storing crucial information and data for the FL process, [30], [39], [56], [61] , verifying correctness of updates [34], [54] or keeping the registry of active members [30], [31], [38], [39], [58], [59] is crucial for the FL workflow and implies coordination through blockchain as an always accessible, verifiable, transparent and immutable infrastructure.…”

Section: Rq 2-2: How Is Blockchain Applied Within the Flf And What Op...mentioning

confidence: 99%

“…aggregating millions of parameters, calculate expansive contribution measurements like Shapley Value or privacy-preserving methods hinder the framework to scale beyond a small group of entrusted entities towards mass adoption. To overcome the scalability-bottleneck of storage, some FLF applied an Interplanetary-File-System (IPFS) [71], where data is stored off-chain in a distributed file system, using the content-address as a unique pointer to each file in a global namespace over all computing devices [32], [34], [39], [42], [45], [50], [51], [60], [64]. Other FLF are based on novel design choices to tackle the scalability-issues: Witt et al [30] applied compressed Federated Knowledge Distillation, storing only 1-bit compressed soft-logits onchain.…”

Section: Rq 2-3: Is the Framework Scalable?mentioning

confidence: 99%

“…[33], [37], [44]), (ii) task requesters (e.g. [36], [45], [56]), (iii) validators whose task is only to measure contribution ( [39], [51]), (iv) blockchain nodes (e.g. [32], [65], [69]), (v) workers (e.g.…”

Section: Rq 3-2: How Are the Contributions Of Workers Measured?mentioning

confidence: 99%

“…First, it was found that the majority of publications do include experiments. Only seven of 40 papers did not conduct experiments [42], [49], [51], [52], [58], [59], [64]. However, the analysis also shows that only 45% of the experiments implement the actual blockchain processes (15 out of 33 papers with experiments).…”

Section: Rq 4-2: How Comprehensive Are the Experiments?mentioning

confidence: 99%

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Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt¹,

Heyer²,

Toyoda³

et al. 2022

Preprint

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The advent of Federated Learning (FL) has ignited a new paradigm for parallel and confidential decentralized Machine Learning (ML) with the potential of utilizing the computational power of a vast number of IoT, mobile and edge devices without data leaving the respective device, ensuring privacy by design. Yet, in order to scale this new paradigm beyond small groups of already entrusted entities towards mass adoption, the Federated Learning Framework (FLF) has to become (i) truly decentralized and (ii) participants have to be incentivized. This is the first systematic literature review analyzing holistic FLFs in the domain of both, decentralized and incentivized federated learning. 422 publications were retrieved, by querying 12 major scientific databases. Finally, 40 articles remained after a systematic review and filtering process for in-depth examination. Although having massive potential to direct the future of a more distributed and secure AI, none of the analyzed FLF is production-ready. The approaches vary heavily in terms of use-cases, system design, solved issues and thoroughness. We are the first to provide a systematic approach to classify and quantify differences between FLF, exposing limitations of current works and derive future directions for research in this novel domain.

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Section: ) Publicmentioning

confidence: 99%

Section: Rq 2-2: How Is Blockchain Applied Within the Flf And What Op...mentioning

confidence: 99%

Section: Rq 2-3: Is the Framework Scalable?mentioning

confidence: 99%

Section: Rq 3-2: How Are the Contributions Of Workers Measured?mentioning

confidence: 99%

Section: Rq 4-2: How Comprehensive Are the Experiments?mentioning

confidence: 99%

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Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt¹,

Heyer²,

Toyoda³

et al. 2022

Preprint

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Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence

et al. 2021

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The COVID-19 pandemic resulted in a significant increase in the workload for the emergency systems and healthcare providers all around the world. The emergency systems are dealing with large number of patients in various stages of deteriorating conditions which require significant medical expertise for accurate and rapid diagnosis and treatment. This issue will become more prominent in places with lack of medical experts and state-of-the-art clinical equipment, especially in developing countries. The machine intelligence aided medical diagnosis systems can provide rapid, dependable, autonomous, and low-cost solutions for medical diagnosis in emergency conditions. In this paper, a privacy-preserving computer-aided diagnosis (CAD) framework, called Decentralized deep Emergency response Intelligence (D-EI), which provides secure machine learning based medical diagnosis on the cloud is proposed. The proposed framework provides a blockchain based decentralized machine learning solution to aid the health providers with medical diagnosis in emergency conditions. The D-EI uses blockchain smart contracts to train the CAD machine learning models using all the data on the medical cloud while preserving the privacy of patients’ records. Using the proposed framework, the data of each patient helps to increase the overall accuracy of the CAD model by balancing the diagnosis datasets with minority classes and special cases. As a case study, the D-EI is demonstrated as a solution for COVID-19 diagnosis. The D-EI framework can help in pandemic management by providing rapid and accurate diagnosis in overwhelming medical workload conditions.

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Secure decentralized peer-to-peer training of deep neural networks based on distributed ledger technology

2020

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Privacy Preserved Decentralized Deep Learning: A Blockchain Based Solution for Secure AI-Driven Enterprise

Cited by 11 publications

References 7 publications

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence

Secure decentralized peer-to-peer training of deep neural networks based on distributed ledger technology

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