The problem AlchemistAI solves

Our project aims to address two key challenges in the healthcare domain

1. Limited availability of high quality medical data:- Medical data such as medical images and EHRs, is essential for training accurate and AI models for various healthcare applications, including disease diagnostics, drug discovery and personalised treatment planning. However, collecting and accessing sufficient amounts of this medical data is a significant challenge
2. Privacy Preservation in Collaborative AI Model Training: Developing accurate and generalizable Al models often requires training on diverse and representative datasets from multiple sources. However, sharing sensitive medical data across organisations raises significant privacy concerns and risks violating data protection regulations. Traditional centralised approaches to data pooling and model training can compromise patient privacy and data sovereignty.

Challenges we ran into

1. Federated Learning Complexity: Implementing a secure and efficient federated learning system demanded overcoming technical hurdles. Custom aggregation techniques were developed within TensorFlow Federated to manage decentralized training effectively. This involved establishing secure communication channels and robust validation mechanisms to preserve data privacy and integrity.
2. Data Privacy and Regulatory Compliance: Handling sensitive medical data required strict adherence to privacy regulations. Stringent data anonymization techniques were implemented, and necessary approvals and consents were obtained before processing real medical data. The use of synthetic data generation and federated learning mitigated privacy risks further. 3. Generating GANs using Pytorch: Designing and training GANs using Pytorch involved exploring various architectures and optimizing the adversarial training process. Ensuring stability during training was crucial to generate high-quality synthetic medical data that accurately reflects real-world scenarios.
3. Validating User-Submitted Data: Robust validation mechanisms were implemented to ensure the integrity of training data. Data format checks, outlier detection, and quality control measures were enforced to verify the correctness and authenticity of data submitted by participating users, preventing potential security risks.
4. Building the Federated Learning Framework: Developing a customized federated learning framework tailored to project requirements was a significant undertaking. This involved designing communication protocols, aggregation algorithms, and secure data transmission mechanisms to securely aggregate model updates from multiple data sources while preserving data privacy.