Please describe your proposed solution.
This proposal is a continuation of our independent research work, in which we created a proof-of-concept decentralized framework to train and deploy edge AI models on the Ethereum blockchain. Our project will be integrated with Cardano for faster, cheaper, and more secure transactions.
The Big Problem
Interactive Web 2.0 is progressively exploiting human decision-making and is unprecedently influencing behavior via centralized AI algorithms. Such large-scale AI systems ought to be built and evaluated to perform a task on a public distributed ledger platform, similar to how primates and humans have successfully evolved higher cognitive intelligence within social constructs. Yet, following in the footsteps of big tech research, AI benchmarks and algorithms rely on centralized datasets and algorithms that pose a threat to secure closed-loop behavior and learning outcomes, both commonly modulated in biological organisms via social interactions.
The AI Conundrum
State-of-the-art AI systems, such as computer vision algorithms, rely heavily on deep learning algorithms trained on large datasets. The most significant strides in computer vision and deep neural networks were spurred by the rise of data-driven systems, leading to some truly astonishing capabilities, from the ability to achieve human-like (and even super-human) levels of performance under ideal viewing conditions on certain vision tasks to the unsettling ability to realistically replace faces and people in high-definition video. However, such cutting-edge data-driven systems require unprecedentedly large datasets and are unlikely to scale with increasing task complexity. The corresponding networks ingesting this data have grown vast in size and scale. Large datasets become difficult to distribute and test against and even more difficult to collect. Only a handful of organizations possess the resources required to collect and generate the cutting-edge datasets used at the forefront of deep learning. Since the volume of data and computational power is often insufficient to train locally, central servers have enabled researchers to train ever-larger networks, optimizing and pushing the limits of deep learning models. These centralized cloud solutions have inherent disadvantages, such as increased data traffic, potential loss of confidentiality, privacy, and security of user data. Consequently, federated learning proposed by Google addresses some of these challenges posed by central learning. In federated learning, the training of AI models is done locally and the model parameters are handled by a central server. This, however, intrinsically makes the entire system vulnerable to a model inversion attack and a single point of failure that halts the federated learning process.
Coming-of-age of Blockchain Technology:
Before the maturing of blockchain platforms, the idea of integrating them with machine learning was limited to marketplaces. Such systems stored already trained models in smart contracts for competitions and did not allow for continual updating and collaborative training. The blockchain smart contract enables model evolution and storage via IPFS, which is typically handled by a central server in federated learning. Without a central entity, blockchain-based federated learning is cryptographically secure while preserving the data privacy of each node.
OUR SOLUTION:
Our current work is implemented on the Ethereum Smart Contract Platform based on the architecture below:
Our framework seamlessly integrates state-of-the-art deep learning models and serves as a good benchmark for blockchain-AI developers. Please watch the Youtube videos to have more information about the literature and how our work is novel and different to existing work.
It has two modes of operations as explained on the Github page. The python script interacts with the blockchain and the IPFS without needing a Solidity interface. This work will be detailed in a publication at IEEE Blockchain and Beyond conference. The future work will address practical real-world considerations while porting to the Cardano blockchain - first via Milkomeda and next via native Plutus implementation.
Please describe how your proposed solution will address the Challenge that you have submitted it in.
Our Edge AI system on the Cardano Blockchain will be able to address two main challenges:-
- Proof of concept for a decentralized edge AI system on a blockchain. AI systems on a blockchain will prove to be more trustworthy than a centralized system.
- We believe our multi-chain, model agnostic framework will open doors for the use and validation of our architecture on actual edge devices and solve real-world problems such as biomedical image processing.
Our current solution on Ethereum incurs high gas fees and low throughput for the nodes of the Decentralized Federated Learning framework. Integrating with Cardano is the natural step for exploiting the advanced EUTXO capabilities besides cheaper and faster throughput.
Our framework guarantees secure, transparent, and fair onboarding of the nodes without the need for a central custodian. The smart contract allows parameter merging with equal rights for all nodes and protects machine learning models from corruption via an incentive mechanism. An immediate application of DFL is privacy-preserving federated machine learning in medicine with the added security of decentralization.
What are the main risks that could prevent you from delivering the project successfully and please explain how you will mitigate each risk?
Risk mitigation will be systematically carried out by creating our Plutus solution in a modular fashion. Thus we maintain code reusability and avoid total system failure due to unforeseen bugs.
One of the main challenges is to prevent model inversion attacks and we propose homomorphic encryption to be incorporated in this smart contract.
Another challenge is to design our Cardano-based framework to fully exploit the E-UTXO model. In particular, handling multiple UTXOs for the different edge nodes while updating the federated model via the smart contract. Our expertise as Plutus Pioneers will be handy and we have successfully handled similar issues in other Cardano projects.