Canton Intelligence Framework Draft

Canton Intelligence Framework

Author: T-RIZE GROUP
Status: Draft
Created: 2026-06-4
Label: TBD
Champion: Eduardo Furtado

Abstract

Canton enables organizations to coordinate assets, workflows, and transactions while preserving privacy, sovereignty, and regulatory compliance.

As artificial intelligence becomes an integral component of valuation, risk management, compliance, underwriting, and investment decision-making, institutions face a new challenge: intelligence remains fragmented because the data required to train effective models cannot be shared.

Simultaneously, transactional volume is growing across the network, validators and applications need to include such intelligence to their processes to effectively support the increased activity.

The Canton Intelligence Framework introduces an open-source infrastructure layer—a scaling solution and developer toolkit—that enables organizations to collaboratively create and govern machine learning models without exposing their underlying datasets.

By extending Canton’s principles of privacy-preserving coordination from assets to intelligence, the framework unlocks a new category of applications and uniquely positions Canton into the AI value chain, establishing a new value stream for the Canton ecosystem.

The result is a reusable ecosystem capability that empowers developers to build scalable, collaborative intelligence while preserving data sovereignty and fostering sovereign AI. Additionally, this opens a new monetization opportunity for private data.

Motivation

Artificial intelligence has become a core component of modern financial infrastructure.

Institutions increasingly rely on machine learning systems for:

• Risk management
• Asset valuation
• Credit underwriting
• Fraud detection
• Compliance monitoring
• Portfolio optimization
• Continuous asset monitoring

However, the data required to improve these systems is fragmented across organizations and often cannot be shared due to regulatory, contractual, privacy, or competitive constraints.

As a result, intelligence remains siloed and many processes remain manual. Organizations independently develop models that are inherently limited by the information available within their own boundaries.

Counterparty Risk Scoring

Financial institutions increasingly rely on blockchain analytics, such as Chainalysis, to assess counterparty risk, detect suspicious activity, and support compliance workflows.

While public blockchains provide transparent transaction histories, effective risk scoring depends on historical investigations, sanctions screening outcomes, fraud reports, transaction patterns, and behavioral analysis accumulated by individual institutions. This intelligence is rarely shared despite being derived from largely public data.

On Canton, participating institutions and validators accumulate valuable intelligence through their own investigations and monitoring activities. Yet this knowledge remains siloed within individual organizations.

Through privacy preserving machine learning, institutions can collaboratively train counterparty risk models that benefit from the collective experience of all participants without revealing sensitive customer information, internal investigations, or proprietary datasets. Knowledge gained by one institution can improve the quality of the shared model while preserving privacy and maintaining regulatory boundaries.

Litigation Finance

Litigation finance is fundamentally a data-driven business. Capital allocation decisions depend on estimating case duration, probability of success, expected recovery values, and portfolio risk.

T-RIZE is already involved in bringing litigation-finance assets onto Canton through institutional digital issuance programs backed by litigation receivables. While individual firms possess historical case data that can improve these predictions, that information remains fragmented across independent organizations and cannot be centralized due to confidentiality requirements, competitive concerns, and legal obligations.

Canton Needs Intelligence

By investing in this capability, Canton can unlock a new category of network activity where organizations collaborate not only through the exchange of assets, but through the creation of intelligence. This expands the range of applications that can be built on Canton and positions the network for a future where artificial intelligence is a core component of financial infrastructure.

Specification

Objective

Develop an open-source Federated Learning framework that enables organizations on Canton to collaboratively create, govern, and monetize machine learning models while ensuring:

• Raw data never leaves its source environment
• Participants retain ownership and control of their information
• Participants decide how much resources to allocate to training
• Training activities are auditable and governed
• Contributions can be measured and rewarded
• Regulatory and privacy requirements are maintained
• Organisations can select their preferred models and fine tune them

The framework introduces a new ecosystem primitive: Coordinated Intelligence.

Just as Canton enables organizations to coordinate complex financial workflows without a single point of failure, the proposed framework enables organizations to coordinate intelligence without centralizing data.

Ecosystem Impact

The Canton Intelligence Framework establishes a new category of applications for the Canton ecosystem. More broadly, the framework expands Canton to infrastructure that coordinates both assets and intelligence.

This creates a new category of network activity in which organizations can collaboratively generate predictive models, risk engines, valuation systems, and decision-support tools while preserving sovereignty over their data.

As additional participants contribute data and expertise, the value of the resulting intelligence can increase without requiring any participant to relinquish control over sensitive information.

Compatibility with established federated learning ecosystems further lowers adoption barriers and enables existing AI practitioners to build on Canton without abandoning familiar tooling.

This positions Canton at the intersection of tokenization, privacy-preserving computing, and artificial intelligence.

Implementation Mechanics

The Canton Intelligence Framework builds upon several years of research and prior implementation experience developing decentralized federated learning infrastructure on EVM-compatible networks.

The architecture separates machine learning execution from coordination and governance. Training, evaluation, and model aggregation occur off-ledger using established federated learning infrastructure, while Canton serves as the system of record for coordination, governance, participation, auditability, and incentive management.

This architectural approach has already been validated through prototype implementations developed by T-RIZE and provides a clear path for bringing collaborative intelligence to the Canton ecosystem.

High-Level Architecture

The framework is composed of four primary components:

Trainer Nodes

Organizations operate trainer nodes within their own environments.

Trainer nodes:

• Maintain custody of training data
• Execute local training workloads
• Generate model commitments
• Participate in evaluations
• Interact with Canton through authenticated identities

Aggregator Services

Aggregators coordinate collaborative training rounds.

Responsibilities include:

• Training orchestration
• Participant selection
• Model aggregation
• Evaluation coordination
• Contribution calculation
• Round finalization

Aggregators coordinate workflows but do not require access to participant datasets.

Serverless GPU & Compute Layer

A scalable compute layer supports:

• Model aggregation
• Federated evaluation
• Benchmarking
• Contribution calculations
• Optional model-serving workloads

This layer enables large-scale collaborative intelligence without requiring every participant to maintain dedicated infrastructure.

Canton Coordination Layer

Canton acts as the authoritative coordination and governance layer.

DAML workflows maintain the canonical record of:

• Training rounds
• Participant permissions
• Governance decisions
• Model commitments
• Evaluation outcomes
• Contribution results
• Incentive distributions

This allows every participant to independently verify system state while preserving data sovereignty.

DAML Coordination Contracts

The framework is expected to include several reusable coordination components.

Potential contract types include:

• TrainingRound – lifecycle management of collaborative training rounds
• ParticipantRegistry – trainer, evaluator, and aggregator membership
• ModelCommitment – verifiable registration of model submissions
• EvaluationRecord – storage of evaluation outcomes
• ContributionRecord – contribution attribution and scoring
• RewardDistribution – incentive settlement and reward allocation
• GovernanceProposal – configurable governance workflows

These contracts collectively establish the system of record for collaborative intelligence.

Recorded Events & Audit Trail

The framework is designed to create a complete audit trail for intelligence creation.

Examples of recorded events include:

• Participant registration
• Training round creation
• Model commitment submissions
• Evaluation submissions
• Contribution calculations
• Reward distributions
• Governance decisions
• Configuration changes

Rather than storing model weights or training data, the framework records verifiable commitments and metadata that establish provenance while preserving privacy.

Deployment Flow

A typical deployment is expected to follow the following process:

1. Establish a collaborative intelligence consortium
2. Deploy coordination contracts and governance configuration
3. Register participants and permissions
4. Configure privacy and evaluation requirements
5. Launch training rounds
6. Record model commitments and evaluations
7. Calculate contributions and distribute rewards
8. Publish auditable results and model lineage

This approach enables repeatable deployment of collaborative intelligence networks while maintaining compatibility with the governance, identity, and privacy guarantees of Canton.

Architectural Alignment

Federated learning and Canton address complementary coordination challenges.

Federated learning enables organizations to collaboratively train machine learning models without sharing raw data. This makes it particularly well suited for regulated industries where privacy obligations, contractual restrictions, and data sovereignty requirements prevent traditional approaches to collaborative intelligence.

These are the same constraints that led organizations to adopt Canton for coordinating assets, workflows, and transactions.

As artificial intelligence becomes increasingly embedded in financial infrastructure, institutions face a similar challenge with intelligence: valuable data exists across organizations, but cannot be centralized. Federated learning extends Canton's privacy-preserving coordination model from assets to intelligence, allowing organizations to collaboratively create models while retaining control over their data.

The relationship is mutually reinforcing.

Federated learning benefits from Canton's existing strengths in identity, governance, selective disclosure, and multi-party coordination. At the same time, Canton benefits from federated learning by expanding the range of activities that can be coordinated across the network.

Together, the two technologies create capabilities that neither provides independently:

• Privacy-preserving intelligence creation
• Transparent governance of machine learning systems
• Auditability and provenance of model development
• Incentive mechanisms for data and model contributions
• Neutral coordination between independent organizations

The result is a foundation for creating, governing, and monetizing intelligence across organizations while preserving privacy, sovereignty, and regulatory compliance.

Security & Privacy

Security and privacy are foundational design principles of the Canton Intelligence Framework.

The framework is intended to support collaborative intelligence creation across a wide range of deployment environments, each with different trust assumptions, regulatory requirements, and risk profiles. Rather than prescribing a single security model, the framework is designed to provide extensible building blocks that allow participants to select the privacy, governance, and security mechanisms appropriate for their use case.

The objective is to provide a flexible foundation capable of supporting the full spectrum of collaborative intelligence deployments, from trusted consortiums to more adversarial multi-party environments.

Threat Model

The Canton Intelligence Framework is designed to support a configurable spectrum of trust assumptions and threat levels.

Different collaborative intelligence deployments operate under different risk profiles. A consortium of regulated financial institutions may have significantly different security requirements than an open ecosystem of independent organizations. As a result, the framework is designed to be extensible and allow participants to select security, privacy, and governance mechanisms appropriate for their deployment.

The framework is intended to support scenarios including:

Trusted Consortiums

Participants are known organizations operating under contractual agreements and regulatory oversight.

Primary concerns may include:

• Operational mistakes
• Unauthorized access
• Data leakage through model updates
• Governance transparency
• Regulatory compliance

Semi-Trusted Ecosystems

Participants are known organizations with partially aligned incentives but limited mutual trust.

Primary concerns may include:

• Data reconstruction attacks
• Strategic manipulation of model contributions
• Free-riding behavior
• Disputes regarding contribution attribution
• Unauthorized model usage

Adversarial Environments

Participants may be economically motivated to manipulate outcomes or gain information from the collaborative training process.

Primary concerns may include:

• Model poisoning attacks
• Sybil attacks
• Membership inference attacks
• Model inversion attacks
• Collusion between participants
• Manipulation of incentive mechanisms

The framework does not prescribe a single trust model or security architecture. Instead, it is designed to support configurable security, privacy, governance, and coordination mechanisms that can be adapted to the needs of individual deployments.

Privacy-Preserving Training

The framework is designed around the principle that raw training data remains under the control of participating organizations.

Depending on deployment requirements, participants may choose to leverage privacy-preserving techniques such as:

• Secure aggregation
• Differential privacy
• Access controls and participant authentication
• Selective disclosure mechanisms

Secure Aggregation

The framework is designed to accommodate multiple secure aggregation approaches based on deployment requirements and trust assumptions.

Potential approaches include:

• Trusted Execution Environments (TEEs)
• Multi-Party Computation (MPC) variants
• Cryptographic secure aggregation protocols

This flexibility allows organizations to balance privacy, performance, operational complexity, and regulatory requirements according to their needs.

Differential Privacy

The framework may incorporate differential privacy mechanisms to help reduce the risk of information leakage from model updates.

Depending on the deployment, privacy budgets and protection mechanisms may be adapted to balance privacy requirements with model utility and performance objectives.

Model Integrity & Poisoning Resistance

The framework is designed to enable mechanisms that help preserve model quality and reduce the impact of malicious or low-quality contributions.

Potential approaches include:

• Federated evaluation workflows
• Anomaly detection on model updates
• Reputation and participation scoring
• Governance-controlled participant admission
• Configurable aggregation and weighting strategies

Auditability & Governance

Training activities, governance decisions, model versions, aggregation events, and participant actions are recorded and independently auditable.

These capabilities can provide:

• Training provenance
• Model lineage
• Contribution attribution
• Governance transparency
• Regulatory traceability

Independent Review

The framework benefits from prior research conducted through T-RIZE Labs and the Industrial Research Chair in Tokenization at École de technologie supérieure (ÉTS), a Canadian university specialized in applied engineering and technology, including work on secure aggregation, differential privacy, federated evaluation, contribution attribution, and incentive mechanisms.

Security assumptions, architectural decisions, and privacy-preserving approaches will be documented to facilitate review by ecosystem participants, researchers, and independent experts.

Backward Compatibility

No backward compatibility impact.

Milestones and Deliverables

All framework components developed under this proposal will be released under the MPL-2.0 License.

Milestone 1 – Foundation

Duration: 4 Months

Milestone 1 builds on T-RIZE’s existing deployed federated learning framework on an EVM-compatible network and prior applied research conducted through T-RIZE Labs and the Industrial Research Chair in Tokenization at ÉTS.

Through T-RIZE Labs and the Industrial Research Chair in Tokenization at ÉTS, several years of research have already been completed by a multidisciplinary team of 12 researchers, 3 professors, and T-RIZE engineers. This work has resulted in multiple peer-reviewed publications and prototype implementations covering all components required for production-grade federated learning systems.

Research areas include secure aggregation techniques (including Trusted Execution Environments and Multi-Party Computation variants), client-to-server assignment strategies, adaptive differential privacy mechanisms, federated evaluation methodologies, transparent contribution attribution through smart contracts, and incentive and compensation models.

As a result, the Canton Intelligence Framework builds upon a substantial body of existing research and implementation experience, allowing the project to focus on integration, productization, and ecosystem adoption rather than fundamental research.

Milestone 1a — Architecture and Specification

Duration: 1 month Deliverables

• Canton-specific architecture and protocol specification

• Daml governance contract specification

• Security and threat model

• Flower interoperability specification

• Development environment and CI/CD pipeline

• Review package including architecture diagrams, implementation roadmap, security assumptions, and Milestone 1b / 1c delivery plan

Success Criteria

• Architecture and security specifications accepted by the Dev Fund Committee

• Repository, development environment, and CI/CD pipeline accessible to the Dev Fund Committee

Milestone 1b — Canton Integration and MVP

Duration: 2 months

Deliverables

• MVP orchestration with Canton integration
• Core Daml contracts for training coordination, governance, and access control
• Testnet auditability records for training rounds, participants, and model versions
• Two-node training workflow on Canton testnet

Success Criteria

• Training round completed on Canton testnet with at least two participating nodes
• Canton records generated for participant authorization, training round creation, model update submission, and governance approval

Milestone 1c — Public Release

Duration: 1 month

Deliverables

• End-to-end reference implementation
• Reusable governance primitives
• Completed auditability layer
• Public MPL-2.0 repository
• Developer documentation and onboarding materials

Success Criteria

• Public repository released and accessible to the Dev Fund Committee
• End-to-end collaborative workflow demonstrated on Canton. Includes Onchain governance, access control, and auditability.
• Developer onboarding materials published

Milestone 2 – Production Deployment and First Adopter

Duration: 4 Months

Deliverables

• Production-ready deployment
• Governance framework
• Operational tooling
• Integration support
• Production pilot implementation

Success Criteria

• At least one successful production deployment for a real world use case
• Two or more organizations participating in a collaborative training
• Public case study published

Milestone 3 – Incentive Layer & Ecosystem Adoption

Duration: 4 Months

Technical Deliverables

• Contribution tracking framework
• Reward distribution mechanisms
• Incentive infrastructure
• Ecosystem participation framework

Ecosystem Deliverables

• Developer onboarding materials
• 2 Technical workshops and demonstrations
• 1 Production case study
• Ecosystem adoption campaign
• Conference presentations and community engagement

Success Criteria

Technical Success

• Reward workflows demonstrated
• Incentive layer released and open-source
• Framework ready for broader ecosystem adoption

Ecosystem Success

• Developer onboarding resources published
• At least one public case study completed
• Community workshops conducted
• Adoption and awareness activities completed in collaboration with the Canton Foundation

Acceptance Criteria

The proposal will be considered complete when:

• All framework components are delivered
• Collaborative training is demonstrated on Canton
• The framework is released as open-source infrastructure
• Documentation and onboarding materials are available
• At least one production deployment is completed
• The framework demonstrates utility beyond T-RIZE internal use

Funding

Funding Structure

Funding for Milestone 1a is requested upon approval of the proposal to support project initiation, team allocation, research activities, and infrastructure development.

Subsequent milestone payments will be released upon successful completion and approval of the preceding milestone.

Milestone	Expected Completion	Funding	Funding Trigger
1a - Architecture and Specification	1 month after acceptance	1,000,000 CC	Upon acceptance of proposal
1b - Canton Integration and MVP	3 months after acceptance	2,000,000 CC	Upon acceptance of Milestone 1b
1c - Public Release	4 months after acceptance	1,500,000 CC	Upon acceptance of Milestone 1c
2 - Prod Deployment	8 months after acceptance	2,500,000 CC	Upon acceptance of Milestone 2
3 - Ecosystem Adoption	12 months after acceptance	3,000,000 CC	50% Upon acceptance of Milestone 2 / 50% Upon acceptance of Milestone 3
TOTAL	12 months	10,000,000 CC

Budget

Line Item	Milestone 1	Milestone 2	Milestone 3	Total
Engineering & Canton Implementation	3,000,000	1,400,000	700,000	5,100,000
Protocol Design, Security Review & Applied Research	600,000	200,000	100,000	900,000
Infrastructure and cloud	400,000	400,000	400,000	1,200,000
Integration and partner support	350,000	400,000	300,000	1,050,000
Ecosystem, marketing, and workshops	150,000	100,000	1,500,000	1,750,000
TOTAL	4,500,000	2,500,000	3,000,000	10,000,000

Total Duration: 12 Months

Co-Marketing

T-RIZE will collaborate with the Canton Foundation on:

• Technical announcements
• Developer workshops
• Educational content
• Conference presentations
• Production case studies

to promote adoption of privacy-preserving AI infrastructure across the Canton ecosystem.

Token Volatility

The funding request is denominated in CC and reflects the estimated resources required to deliver the proposed milestones.

In the event that the market value of CC experiences a material increase or decrease of more than 30% relative to its value at the time of proposal approval, the Canton Foundation and T-RIZE may review future milestone payments in good faith to ensure the continued viability of the project while preserving the intent of the original funding commitment.

Any adjustments would apply only to future milestone payments and remain subject to mutual agreement.

Why T-RIZE

This proposal builds on several years of research and development conducted by T-RIZE and its academic partners in distributed systems, blockchain infrastructure, and federated learning.

T-RIZE has already developed and deployed foundational components of decentralized federated learning infrastructure on EVM-compatible networks, including model governance, contribution attribution, incentive mechanisms, access controls, and blockchain-based coordination of training activities. This prior work significantly reduces execution risk and accelerates delivery of the Canton Intelligence Framework.

In 2023, T-RIZE and ÉTS established the Industrial Research Chair in Tokenization, led by Professor Kaiwen Zhang and later joined by Professors Edward Zhang and Sara Rouhani. The Chair secured more than CAD $3 million over five years to advance research in tokenization, privacy-preserving computing, distributed federated learning, and collaborative intelligence systems. This research program has produced multiple peer-reviewed publications and prototype implementations that directly inform the Canton Intelligence Framework.

Beyond its research efforts, T-RIZE actively structures and administers institutional digital assets on Canton, including litigation-finance-backed issuance programs and other real-world asset initiatives where collaborative intelligence can improve valuation, underwriting, risk assessment, and capital allocation.

The Canton Intelligence Framework converts this experience into reusable open-source infrastructure for the broader Canton ecosystem.

Long-Term Sustainability

The Canton Intelligence Framework will be released as open-source under MPL-2.0 license and developed in collaboration with the broader Canton ecosystem.

T-RIZE intends to continue maintaining and evolving the framework beyond the grant period as part of its broader tokenization and privacy-preserving AI initiatives. The framework directly supports use cases already being developed by T-RIZE and its partners, creating strong incentives for ongoing maintenance and improvement.

Long-term sustainability will be driven by three factors:

• Adoption by Canton applications, validators, and asset issuers
• Continued research and development through T-RIZE's Industrial Research Chair in Tokenization
• Commercial deployments that benefit from and contribute to the framework's evolution

As adoption grows, the framework is expected to become foundational infrastructure for collaborative intelligence applications within the Canton ecosystem.