FHE.Chess ready

contribution to ZAMA.ai

FHE.Chess (Fully Homomorphic Encryption Chess app)
v1.0.0 (FHE ready)

• Chess_app
• Client-Server architecture :
  • client: local machine with Chess_app + network
  • Server: remote machine with training data and scripts, deployed models in proper client-server FHE architecture, weights, …
• Dataset: White and Black of rating 2000 ELO
• Normal and Quantized AI (2 models: SOURCE and TARGET)
• 2 main modes:
  • WhiteAI (AI as white vs Human as black)
    • 3 contextual modes: clear, simfhe, deepfhe
  • White H (Human as white vs Human as black)
• All documentation

Detailed of the 3 contextual modes:
- clear: non-encryption context. Fast inferences,
- simfhe: encrypted context thanks to simulation of FHE computations. Fast inferences,
- deepfhe: encrypted context thanks to FHE. Very slow inferences (because model complexity + hardware),
AI mainly starts its moves the same way, after several white & black moves its moves are relevant.

• Reported bugs:

  • false positive authorized en-passant moves,
  • false positive authorized move of kings even if the targeted (aka destination) square conducts to check,
  • because of the dual chess developments: one from scratch, one from python-chess, the latter crashes the Chess app when a move is not "legal".

• To Do:

  • migrate current architecture to future architecture (client(local): chess_app + client FHE (private keys) | server(remote): models + server FHE (public keys)),
  • resolve false positive authorized moves.

• Job done:

  • Quantized AI (2nd model TARGET) trained on data
  • Simulation VL + FHE
  • Adding training & test metrics within documentation
  • Fixed Chess App. bugs + add outcome
  • Wrap the client-server into Docker containers (done but not necessary then deleted)
  • Detailed documentation + add quantized, sim. VL and FHE explanation
  • re-implement final FHE AI (aka OFHE_ZV bot) to fit with Lichess API for ranking (out of scope)