At the same time, we need to consider important standards relating to data privacy and protection, such as Convention 108+ of the Council of Europe17. Self-driving cars may have a number of sensors that generate several petabytes of data per day. When data is generated at large, distributed locations, it is very difficult to aggregate it to a central location, such as the cloud, for centralized machine learning. “It combines the GPUs with service nodes and the option of storage in the form of the HPE Parallel File System Storage solution, and then connections into the enterprise via the Aruba 6300 switch.” This, he said, was the most basic configuration of the MLDS. Hotard pointed to a use case with TigerGraph, a provider of graph database and graph analytics software. The company combined HPE Swarm Learning with its data analytics platform to augment efforts in detecting unusual activity in credit card transactions.
Model users are the primary consumers in MSL who pay to access the healthcare services provided by the AI global models of MSL. In this bona fide effort to collaborate and improve the overall health of humanity, a centralized machine learning approach faces significant risks in terms of regulatory compliance. Existing regulations governing medical records in the countries where each institution is located may hinder the sharing of real data and obtaining approval for its transfer to a central location that could possibly be outside the country or origin.
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C, The principles of the SL workflow once the nodes have been enrolled within the Swarm network via private permissioned blockchain contract and dynamic onboarding of new Swarm nodes. Indeed, statements by lawmakers have emphasized that privacy rules apply fully during a pandemic43. Particularly in such crises, AI systems need to comply with ethical principles and respect human rights12. Systems such as SL—allowing fair, transparent, and highly regulated shared data analytics while preserving data privacy—are to be favoured. SL should be explored for image-based diagnosis of COVID-19 from patterns in X-ray images or CT scans15,16, structured health records12, or data from wearables for disease tracking12. We decreased case numbers at node 1 further, which reduced test performance for this node (Extended Data Fig. 7e), without substantially impairing SL performance.
- Inspired by biology, swarm learning is based on blockchain, and is designed to ensure that only legitimate participants join a decentralized learning network.
- Metaverse applications in healthcare can realize patient access triage and further reduce the burden on the healthcare system, thereby directing scarce healthcare resources to patients with the most urgent healthcare requirements.
- Although the experimental results show that the accuracy of MSL model decreases slightly as the number of SLNs involved in training increases.
- Both are aimed at easing the burdens of AI development in a development environment that increasingly features large amounts of protected data and specialized hardware.
- One is that the nodes with swarm learning ability in the physical healthcare world are often endorsed by fundamental healthcare institutions, which would have more anonymous avatars in the metaverse.
Each node within this simulation could stand for a medical centre, a network of hospitals, a country or any other independent organization that generates such medical data with local privacy requirements. As an alternative, we introduce SL, which dispenses with a dedicated server (Fig. 1d), shares the parameters via the Swarm network and builds the models independently on private data at the individual sites (short ‘nodes’ called Swarm edge nodes) (Fig. 1e). SL provides security measures to support data sovereignty, security, and confidentiality (Extended Data Fig. 1a) realized by private permissioned blockchain technology (Extended Data Fig. 1b). Each participant is well defined and only pre-authorized participants can execute transactions. Onboarding of new nodes is dynamic, with appropriate authorization measures to recognize network participants.
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The parameters can be merged as average, weighted average, minimum, maximum, or median functions. In addition, SL can cope with biases such as sex distribution, age or co-infection bias (Extended Data Fig. 10a–c, Supplementary Information) and SL outperformed individual nodes when distinguishing mild from severe COVID-19 (Extended Data Fig. 10d, e). Collectively, we provide evidence that blood transcriptomes from COVID-19 patients represent a promising feature space for applying SL. Staking an early claim in “the next gold rush for machine intelligence,” HPE today announced the launch of HPE Swarm Learning. This privacy-preserving, decentralized machine learning (ML) framework for the edge and distributed sites was developed by its R&D organization Hewlett Packard Labs.
In which PM is merged parameters, Pk is parameters from the kth node, Wk is the weight of the kth node, and n is the number of nodes participating in the merge process. Yet, the divide between what is possible and what global privacy legislation allows, has widened. Learn how HPE’s Swarm Learning fundamentally changes the ML paradigm, is distinguished by blockchain, and opens novel opportunities for collaboration across boundaries.
Supplementary Table 6
Multiple methods of merging are supported here, including average, weighted average, and median. The reader combines the parameter values of all nodes using the selected merge algorithm and informs the other nodes of the merged parameters. Each node then downloads a file from the leader, and updates its local model with a set of new parameter values. By only sharing learnings, the tool allows users to leverage large training datasets without compromising privacy.
While beneficial from an AI perspective, centralized solutions have inherent disadvantages, including increased data traffic and concerns about data ownership, confidentiality, privacy, security and the creation of data monopolies that favour data aggregators19. Consequently, solutions to the challenges of central AI models must be effective, accurate and efficient; must preserve confidentiality, privacy and ethics; and must be secure and fault-tolerant by design23,24. Data are kept locally and local confidentiality issues are addressed26, but model parameters are still handled by central custodians, which concentrates power. In MSL system, the SNNs are the consensus nodes for the blockchain network in MSL, and they are fully trusted among all SLNs participating in our framework.
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Bitcoin is a peer-to-peer cash system that creates a decentralized, immutability, and traceable payment system. In bitcoin, each transaction will be broadcast into the network by both parties, and the nodes in the network will then record transactions to their ledgers. When a miner swarm learning services in the network completes a proof of work (PoW) task, its block can link to the previous block, thus forming a blockchain. In the blockchain, each transaction is completed peer-to-peer, and no third-party trusted node endorses the transaction, which realizes decentralized.
The smart contract is a string of code that can be executed on the machine [18], allowing transactions to be performed without a third party. Smart contracts can be executed automatically when the characteristic conditions are met and do not require the approval of a trusted entity. Ethereum is the first to introduce a smart contract that provides a turing-complete machine – Ethereum virtual machine (EVM) [19], which supports people on Ethereum to develop complex smart contracts and deploy them to the public blockchain. In today’s machine learning, data is sent to a central data center or cloud for aggregation, training, and model creation. The models created at the central location are then deployed to the edge environment, where the data is generated. Here, inferences are determined and decisions are made based on real-time predictions, and autonomous actions are taken.
2 Model sharing in MSL
Moreover, the decentralized autonomous organization blockchain network is proposed to guarantee the fairness of model sharing gains among the imbalance of healthcare resource data. Simulation results on two practical healthcare datasets show that our proposed model-sharing can achieve better accuracy than local training and approximate accuracy compared to central training. This article proposes a swarm learning-based healthcare model sharing framework to promote the security and fairness of healthcare model generation and application in the metaverse.
Swarm Learning, HPE said, is effectively composed of a set of APIs and is purely software-based—and can be integrated with the MLDS, as well. All told, the hope is that the swarm learning concept will foster “AI for the greater good” by encouraging collaboration across organizations and around the globe, Hotard said. He added that it’s a mission of HPE is to make AI more heterogeneous by removing complexities of ML development and enabling ML engineers to build models at greater scale. When applied to intelligent devices operating in the real world, “swarm learning” refers to decentralization.
The Swarm Learning framework
Yet, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation5,9,10. Particularly in a global crisis6,7, reliable, fast, secure, confidentiality- and privacy-preserving AI solutions can facilitate answering important questions in the fight against such threats11,12,13. AI-based concepts range from drug target prediction14 to diagnostic software15,16.
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We first design a parameter merging mechanism for model sharing to improve the probability of optimizing local models of the swarm learning nodes with poor healthcare data resources. Finally, we use two real-world healthcare datasets to verify the validity of the proposed algorithm. First, the patient data in the metaverse generally contain sensitive information, such as healthcare records, healthcare images, genetic data, et al. Therefore, security and privacy must be ensured when using these data in metaverse to avoid data leakage and misuse. Further, healthcare data is generated from different sources with different formats, which leads to data standardization and interoperability issues.
The other is the swarm learning nodes with high-quality healthcare data resources will be more inclined to train local healthcare-AI models and provide healthcare services rather than participating in swarm learning due to profit considerations. On the contrary, the swarm learning nodes with poor healthcare data resources will participate in swarm learning. The imbalance between the payment of data resources and the benefits of model sharing will result in fairness issues. We hypothesized that completely decentralized AI solutions would overcome current shortcomings, and accommodate inherently decentral data structures and data privacy and security regulations in medicine.