234 points by codingnerd 7 months ago flag hide 11 comments
decentralized_ai 7 months ago next
I'm the original poster and I'm thrilled to share how I built a decentralized AI platform in just 3 months. It's an exciting journey with ups and downs, and I can't wait to share more details with you all. AMA!
nate17 7 months ago next
Great job! Could you share some more insights on the challenges you faced while building it on a decentralized network? I'm looking forward to hearing more about the story.
decentralized_ai 7 months ago next
@nate17 Sure! The main challenge was ensuring the network consistently trained models across different nodes while maintaining a trustless environment. To solve this, I implemented a clever consensus algorithm that fostered training convergence, and it's been working very well. I think this is being underutilized in the decentralized ML space, which presents a great opportunity for advancement.
blockchain_enthusiast 7 months ago prev next
Would love to learn about your decision-making process for choosing a consensus algorithm. Were there any other alternatives you considered or benchmarked against? Please share!
decentralized_ai 7 months ago next
@blockchain_enthusiast The decision process was based on several factors, including scalability, fault tolerance, and implementation complexity. I weighed the options between Proof-of-Stake (PoS) and Proof-of-Authority (PoA) and landed on PoA-based consensus. I went ahead with the Clique PoA mechanism due to its low entry barrier and quick sync capabilities while still ensuring consensus quality. I also explored PoW briefly, but the energy consumption and latency ultimately led to its dismissal.
crypto_fan 7 months ago prev next
As a curious developer, I'm interested in knowing the software tools and libraries you used to get started. It's impressive that you were able to build it in just three months, and I think knowing that info would be very helpful to the community. Thank you!
decentralized_ai 7 months ago next
@crypto_fan Thank you! I can certainly share the tools and libraries I've used. I leaned heavily on the following: • TensorFlow for machine learning development • Open Ethereum Pool Monitor for the PoA network setup • Web3.py for communicating with the blockchain in Python • Docker for containerization and simplified deployment • Jupyter Notebooks for exploratory development and AI research By combining these resources, I quickly iterated and put together the MVP. I believe that the rapid iteration was possible due to the maturity of the AI and blockchain ecosystems.
sherlock_developer 7 months ago prev next
This is amazing! I particularly want to know more about the testing, integration, and deployment aspects. With so many different components, I can imagine it being quite challenging. Would you tell us more about your strategy for this? Thank you.
decentralized_ai 7 months ago next
@sherlock_developer Sure, testing and deployment were indeed challenging, with multiple components and services. For testing, I utilized TDD and applied testing in isolation to ensure reliability and reproducibility. I used various testing frameworks including the Testinfra, Pytest, and Unittest for different aspects such as containerized environment, REST APIs, and machine learning integration. For deployment, I applied GitOps and utilized Kubernetes and Helm for container orchestration, along with CircleCI for CI/CD pipelines. This made it easier to manage applications and ensured versioned deployment, easy rollbacks, and reproducible deployment.
jeel_techie 7 months ago prev next
One question regarding user privacy and data handling. How do you protect data privacy while handling potentially sensitive user data throughout the machine learning lifecycle? Thank you.
decentralized_ai 7 months ago next
@jeel_techie Data privacy was a critical factor in the design of the system. To address this, I incorporated local differential privacy for data analysis and AlfredChen's secure aggregation technique for federated learning that secures data even further. The platform users can share data privately without compromising their privacy. I also added encryption in transit and at rest to ensure higher data security levels. Personally-identifiable data is hashed, salted, and securely partitioned to ensure anonymity when training AI models using user data.