The problem TuskNet solves

Federated learning revolutionizes AI in healthcare by enabling institutions to collaborate on training models without sharing raw data, addressing privacy and regulatory concerns. It allows hospitals to retain control over sensitive information while contributing to global advancements, crucial for breakthroughs in diseases like cancer.

Key Advantages
Privacy-First: Data stays local; only encrypted updates are shared, complying with GDPR, HIPAA, and other regulations.
Diverse Data Access: Models learn from varied populations, reducing bias and improving inclusivity.
Efficiency: Cuts costs and energy use by eliminating data transfer needs.
Regulatory Compliance: Facilitates global collaboration without breaching privacy laws.
Impact on Cancer Research
Federated learning accelerates progress in early detection, personalized treatments, and diagnostics by pooling knowledge from diverse datasets. This results in more accurate, inclusive, and sustainable AI solutions, empowering researchers and institutions to collaborate responsibly while safeguarding patient privacy.

The future of healthcare lies in secure, intelligent collaboration—federated learning is leading the way.

Challenges we ran into

Encryption Complexity
One of the significant technical hurdles was implementing and synchronizing cryptographic schemes like Pedersen Commitments, Paillier Cryptosystem, and AES Encryption. Each of these methods serves a unique purpose: Pedersen Commitments ensure secure verification, Paillier supports homomorphic encryption, and AES provides fast, secure encryption for sensitive data. Combining these into a cohesive system was challenging due to their varying computational requirements and mathematical foundations. Extensive testing was required to ensure compatibility and maintain the security of data and model updates during training and aggregation processes.

Decentralization vs. Efficiency
Maintaining decentralization while ensuring efficiency posed a complex trade-off. Blockchain, used for critical functions like aggregation and verification, is transparent and secure but slow and expensive for large-scale computations. Off-chain operations like model training and data storage offer better performance but must still align with the decentralized vision. Striking the right balance—keeping critical processes on-chain while offloading heavy computations—required careful architectural planning to maintain transparency, cost-efficiency, and scalability.

Lack of Established Resources
Federated learning in healthcare is still an emerging field, and there is a scarcity of practical implementations to use as references. Developing the system required delving into academic research papers on cryptography, federated learning, and decentralized architecture. Translating theoretical concepts into functional code while ensuring robustness was time-consuming and intellectually demanding.