aichannel

this is why Bagel - Monetizable Open Source AI™ is so out of this world!

let's explore the paper that bidhan roy (Bagel's Founder) shared.

ZKLoRA addresses 02 critical requirements in untrusted, distributed training environments ...

↪️ i read so you don't have to 👇🧵

🙌 Technical Implementation

03-step verification process:

1. Multi-Party Inference between base model user and LoRA contributor
2. Proof generation by the LoRA contributor
3. Verification by the base model user

Technical Innovations:

- Cryptographic Circuit Compilation for LoRA transformations
- Recursive Proof Systems (Nova/HyperNova)
- Incrementally Verifiable Computation (IVC)

🙌 Results & Efficiency

- Successfully tested across various model sizes (from DistilGPT2 to 70B parameter models)
- Verification time scales linearly but remains practical (80 modules verified in minutes)
- Benchmark results show verification times of 31-74 seconds per module for large models like LLaMA-70B and Mixtral-8x7B

🙌 Applications & Benefits

- Enables secure collaboration in decentralized teams
- Supports contract-based training pipelines
- Protects intellectual property while ensuring compatibility
- Privacy-preserving verification with deterministic guarantees

🙌 Future Directions

- Adding polynomial commitments for end-to-end verifiability
- Supporting multi-contributor LoRAs
- Enhancing data privacy
- Integrating advanced ZKPs for performance improvements

**Conclusion:** ZKLoRA successfully bridges the gap between privacy and practicality in AI model fine-tuning, enabling secure and efficient collaboration while protecting intellectual property.

🙌 Key Features & Technical Solution

ZKLoRA Protocol Components:

- Verification takes only 1-2 seconds per LoRA module, even with large language models
- Uses zero-knowledge proofs and Multi-Party Inference for verification
- Provides deterministic correctness guarantees

Multi-Party Inference Process:

- Base model user and LoRA contributor exchange partial activations ("Base Acts" and "LoRA Acts")
- LoRA contributor processes activations without revealing low-rank matrices
- Generates cryptographic proofs for verification