3 research outputs found
Ownership preserving AI Market Places using Blockchain
We present a blockchain based system that allows data owners, cloud vendors,
and AI developers to collaboratively train machine learning models in a
trustless AI marketplace. Data is a highly valued digital asset and central to
deriving business insights. Our system enables data owners to retain ownership
and privacy of their data, while still allowing AI developers to leverage the
data for training. Similarly, AI developers can utilize compute resources from
cloud vendors without loosing ownership or privacy of their trained models. Our
system protocols are set up to incentivize all three entities - data owners,
cloud vendors, and AI developers to truthfully record their actions on the
distributed ledger, so that the blockchain system provides verifiable evidence
of wrongdoing and dispute resolution. Our system is implemented on the
Hyperledger Fabric and can provide a viable alternative to centralized AI
systems that do not guarantee data or model privacy. We present experimental
performance results that demonstrate the latency and throughput of its
transactions under different network configurations where peers on the
blockchain may be spread across different datacenters and geographies. Our
results indicate that the proposed solution scales well to large number of data
and model owners and can train up to 70 models per second on a 12-peer non
optimized blockchain network and roughly 30 models per second in a 24 peer
network
A Review on Building Blocks of Decentralized Artificial Intelligence
Artificial intelligence is transforming our lives, and technological progress
and transfer from the academic and theoretical sphere to the real world are
accelerating yearly. But during that progress and transition, several open
problems and questions need to be addressed for the field to develop ethically,
such as digital privacy, ownership, and control. These are some of the reasons
why the currently most popular approaches of artificial intelligence, i.e.,
centralized AI (CEAI), are questionable, with other directions also being
widely explored, such as decentralized artificial intelligence (DEAI), to solve
some of the most reaching problems. This paper provides a systematic literature
review (SLR) of existing work in the field of DEAI, presenting the findings of
71 identified studies. The paper's primary focus is identifying the building
blocks of DEAI solutions and networks, tackling the DEAI analysis from a
bottom-up approach. In the end, future directions of research and open problems
are proposed.Comment: This work has been submitted to the IEEE for possible publication.
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Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing
Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence