Project 2: Federated Learning and Privacy

The Challenge: Synthesizing from Sensitive Data

Many of the most valuable applications for CNS 2.0 involve synthesizing information from sensitive or proprietary data sources. For example:

• Multiple pharmaceutical companies might want to collaborate on synthesizing research to find a new drug, but they cannot share their internal experimental data.
• Intelligence agencies from allied nations might need to fuse threat intelligence without revealing their sources and methods to one another.
• Corporations might want to synthesize market analysis without sharing confidential business strategies.

A centralized architecture, where all data must be sent to a single server for processing, makes these use cases impossible.

The Vision: A Decentralized Knowledge Ecosystem

This research project aims to design and develop a decentralized, federated architecture for CNS 2.0. In this model, SNOs would be stored and processed locally within each organization’s secure environment. The system would enable collaborative synthesis without ever exposing the raw, underlying evidence to other parties, moving from a centralized data model to a distributed reasoning network.

Key Research Questions

1. How can we design a protocol for two or more parties to collaboratively generate a synthesis SNO without revealing their private evidence sets?
2. What cryptographic or privacy-preserving techniques (e.g., Secure Multi-Party Computation, Homomorphic Encryption, Differential Privacy, Zero-Knowledge Proofs) are best suited for this task?
3. How can the CriticPipeline operate in a federated setting? For example, how can the GroundingCritic assess a claim’s evidence if it cannot see the evidence?
4. How can we build a trust and provenance system that is reliable in a decentralized network?

Proposed Methodology

This research will integrate cutting-edge techniques from privacy-preserving AI to build a robust, secure, and decentralized CNS 2.0 architecture. The methodology, drawn from the proposals in the IdeasPaper (Sec 8.3), is structured as follows:

Stage 1: Federated Protocol Design

The core of this project is the design of a novel protocol for privacy-preserving synthesis. This is not just federated learning, but a federated reasoning system.

• Dialogue Protocol: We will design a multi-agent dialogue protocol that allows agents representing different organizations to negotiate the synthesis process. This includes steps for proposing SNOs for synthesis, agreeing on evaluation metrics, and collaboratively generating the final SNO_Synthesis.
• Privacy-Preserving Computations: The protocol will incorporate a suite of advanced cryptographic techniques:
  1. Secure Multi-Party Computation (SMPC): To allow agents to jointly compute CScore (chirality) and EScore (entanglement) on their private SNOs. This enables the system to identify ideal synthesis candidates without revealing the underlying hypothesis embeddings or evidence sets.
  2. Differential Privacy: To add statistical noise to any shared metadata or aggregate scores, making it impossible to reverse-engineer information about a specific SNO or piece of evidence from a participating organization.
  3. Zero-Knowledge Proofs (ZKPs): To solve the critical problem of federated evaluation. An agent will be able to generate a ZKP to prove that its local SNO is well-grounded (i.e., it achieved a high score from its internal GroundingCritic) without revealing the sensitive evidence itself.
• Trust and Provenance Mechanisms:
  • Blockchain for Provenance: We will explore using a private, permissioned blockchain to create an immutable, auditable log of all synthesis operations and SNO lineage across the federated network. This ensures that all participants have a shared, trustworthy record of how a given synthesis was created.

Stage 2: Proof-of-Concept Implementation and Simulation

• Simulation Environment: We will build a simulation of the federated CNS 2.0 network, allowing us to model multiple organizations with distinct, private SNO populations and varying levels of trust.
• Protocol Implementation: We will implement a proof-of-concept version of the federated synthesis protocol, likely using existing libraries for SMPC, ZKPs, and differential privacy to accelerate development.
• Key Demonstration: The primary goal is to demonstrate that two simulated organizations can successfully generate a high-quality synthesis SNO that resolves a conflict between their private narratives. The final SNO_Synthesis must be verifiable and trusted by both parties, even though neither had access to the other’s source material.

Stage 3: Performance, Security, and Scalability Analysis

• Performance Benchmarking: We will rigorously measure the computational and network overhead of the federated protocol compared to the centralized baseline. The key metric will be the “privacy vs. performance trade-off,” quantifying the cost of the privacy-preserving features.
• Security Auditing: We will conduct a thorough security analysis of the protocol, using threat modeling to identify potential information leakage vectors, collusion attacks, or other vulnerabilities.
• Scalability Testing: We will test the protocol’s performance as the number of participating organizations and the size of their SNO populations grow, identifying potential bottlenecks for future optimization.

Expected Contribution

A federated architecture for CNS 2.0 would be a groundbreaking achievement, representing a major contribution to the fields of privacy-preserving AI and trustworthy multi-agent systems. It would unlock a vast range of collaborative knowledge discovery applications—in medicine, finance, national security, and beyond—that are currently impossible due to privacy and security constraints. By solving the challenge of synthesizing insights from data that cannot be shared, this research would transform CNS 2.0 from a powerful analytical tool into a secure platform for multi-organizational collaboration and knowledge creation.