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# CreditGraphFormer & RegulaLogic: Privacy-Aware Federated Enterprise Credit Rating This repository provides a reference implementation of **CreditGraphFormer** and **RegulaLogic**, a unified framework for **privacy-aware federated enterprise credit rating with fiscal big data**. It is inspired by the work *"Privacy-Aware Federated Learning for Enterprise Credit Rating with Fiscal Big Data"* by **Xuan Huang**, which integrates fiscal knowledge graphs, transformer-based graph encoders, and regulatory reasoning modules to deliver accurate and interpretable credit scores under strict privacy and compliance constraints. :contentReference[oaicite:1]{index=1} The framework is designed for scenarios where enterprise tax bureaus, financial institutions, and regulators must collaborate on credit modeling without centralizing raw fiscal data. --- ## 1. Motivation Modern enterprise credit rating faces three key challenges: - **Data Privacy** Fiscal records, tax filings, and transaction logs are highly sensitive. Regulations such as GDPR and local financial data laws restrict direct data sharing and central aggregation. - **Heterogeneous Fiscal Big Data** Enterprise behavior is represented by multi-source, multi-view data: - Tax declarations, invoices, and audit records - Corporate registration information and legal events - Supply-chain and transaction networks These data are high-dimensional, sparse, and structurally complex. - **Regulatory Compliance & Interpretability** Credit ratings must be: - Transparent to regulators - Consistent with policy rules and audit histories - Robust against distribution shifts in decentralized environments CreditGraphFormer and RegulaLogic together address these issues by combining **heterogeneous graph representation learning**, **temporal stability modeling**, **federated optimization**, and **rule-aligned reasoning**. --- ## 2. Conceptual Overview The framework consists of three conceptual layers: 1. **CreditGraphFormer** A transformer-style graph encoder operating on a **fiscal knowledge graph**: - Nodes: enterprises and regulatory entities - Edges: typed relations such as invoice issuance, supply-chain links, equity relations, legal representation, etc. - Attributes: revenue, VAT behavior, audit frequencies, penalties, industry and regional codes :contentReference[oaicite:2]{index=2} Key ideas: - Multimodal feature projection into multiple fiscal “views” - Relation-specific message passing with attention over neighbors - Regulatory-aware transformer blocks with compliance masks - Temporal stability embeddings based on indicator volatility over time 2. **RegulaLogic** A compliance-driven reasoning layer that adjusts neural predictions using: - Compliance coefficients (filing timeliness, penalty frequency, audit signals) - Constraint masks and deterministic downgrade rules - Sector-based rating floors and policy penalties - Temporal smoothing of ratings and confidence calibration :contentReference[oaicite:3]{index=3} 3. **Federated Learning Pipeline** A privacy-preserving training scheme where: - Multiple clients (institutions) keep data local - Models are trained locally and aggregated securely - Optional differential privacy and secure aggregation mechanisms are used to prevent leakage of sensitive gradients. --- ## 3. Key Components ### 3.1 Multimodal Feature Projection Enterprise attributes are projected into multiple fiscal “views” (e.g., taxation profile, invoice behavior, audit trail): - Each view has its own projection matrix. - View embeddings are concatenated into a composite representation. - Relation-specific attention modules aggregate information from neighbors based on relation type. :contentReference[oaicite:4]{index=4} This allows the encoder to exploit domain structure instead of treating all features as a flat vector. --- ### 3.2 Regulatory-Aware Transformer Encoder CreditGraphFormer uses a transformer-style encoder that: - Operates on aggregated relational messages. - Applies a **regulatory mask** that: - Suppresses contributions from non-compliant or high-risk neighbors. - Enforces compliance-aware attention patterns. - Produces contextualized node embeddings that already respect policy-level constraints. This design connects low-level fiscal indicators with high-level regulatory semantics. --- ### 3.3 Temporal Stability Embedding Credit behavior is inherently temporal. The framework: - Computes temporal differences of fiscal indicators over a time window. - Aggregates volatility into a **stability embedding**. - Injects stability signals into the final representation, allowing: - Distinction between structurally stable and highly volatile enterprises. - Better robustness to noisy or transient events. :contentReference[oaicite:5]{index=5} --- ### 3.4 RegulaLogic Reasoning Layer RegulaLogic refines CreditGraphFormer outputs according to explicit rules: - **Compliance Coefficient and Constraint Mask** - Enterprises with low compliance (e.g., late filings, frequent penalties) receive rating penalties. - A binary mask gates compliant vs. non-compliant entities. - **Audit-Based Downgrades** - When audit risk exceeds a threshold, ratings are deterministically downgraded by at least a fixed step. - **Sector-Based Rating Floors** - Each industry sector can define a minimum rating floor, ensuring sector-consistent decisions. - **Temporal Smoothing and Policy Penalties** - Historical predictions are smoothed with an exponential decay kernel. - Policy rules generate a penalty score that further adjusts the smoothed rating. :contentReference[oaicite:6]{index=6} - **Confidence Calibration** - High-entropy predictions can be replaced with sector median ratings, improving stability and regulatory trust. --- ## 4. Typical Workflow A typical usage pattern for this project is: 1. **Data Preparation** - Build a fiscal knowledge graph from: - Tax records, invoices, registrations, and audit logs. - Encode: - Node attributes (enterprise features). - Relation types (edge labels). - Temporal slices of indicators. 2. **Federated Setup (Optional)** - Partition data by institution (e.g., different tax bureaus or banks). - Initialize federated clients that each train a local CreditGraphFormer. - Configure secure aggregation and optional differential privacy. 3. **Model Training** - Train CreditGraphFormer with an **ordinal loss** for rating labels. - Add regularization terms for graph smoothness and compliance alignment. - Periodically run federated aggregation if in decentralized mode. 4. **RegulaLogic Inference** - Feed encoder outputs and metadata (compliance coefficients, sector tags, audit signals) into RegulaLogic. - Obtain adjusted logits, calibrated probabilities, and final discrete ratings. 5. **Evaluation** - Use metrics such as: - Accuracy, macro and weighted F1, AUC. - Default detection rates and precision@k. - Stress-test under label noise, distribution shift, and different privacy budgets. --- ## 5. Example Use Cases - Enterprise tax and credit rating for government fiscal regulators. - Credit risk assessment for financial institutions with strong privacy constraints. - Cross-region or cross-sector credit modeling without pooling raw data. - Research on trustworthy and regulation-compliant AI in financial governance. --- ## 6. Limitations and Future Directions - **Rule Maintenance** RegulaLogic currently relies on manually specified policy rules. As regulations evolve, rules must be updated or partially learned from data. - **Federated Assumptions** The framework assumes reasonably honest participants and stable communication. Adversarial clients and highly unstable networks require more robust protocols. - **Scalability** Very large fiscal graphs and complex rule sets may require: - Hierarchical modeling strategies. - Further compression and sampling techniques. Future work may explore dynamic rule learning, stronger cryptographic protections, and automated monitoring of model–policy alignment.

# FairCoDNet: Fairness-Constrained Financial Regression Model FairCoDNet is an interpretable and fairness-aware financial regression architecture designed to provide equitable, transparent, and high-performance predictions under sensitive-attribute constraints. The framework is built upon the principles described in the paper *“Study on the enterprise tax risk identification model based on multi-source fiscal and tax data”* by **Xuan Huang**, integrating multi-source data fusion, fairness-driven optimization, and convex reformulations to ensure strong predictive ability without violating fairness constraints. This repository aims to provide an easy-to-extend implementation of the model components, including coefficient-decoupled regression layers, fairness-aware CoD constraints, and a novel Convex Reformulation & Constraint-Aware Optimization Dynamics (CAOD) framework. --- ## ✨ Key Features ### **Fairness via CoD Constraint** FairCoDNet explicitly restricts the proportion of predictive variance that can be explained by sensitive attributes such as gender, age, region, or protected financial indicators. The fairness constraint is implemented using: - Coefficient of Determination (CoD) between predictions and sensitive attributes - Tunable fairness threshold ε - Second-order cone (SOC) reformulation for tractable optimization --- ### **Coefficient Decoupling Strategy** The regression head decomposes prediction coefficients into: - A sensitive feature component - A non-sensitive feature component The mixing factor γ ∈ [0,1] allows smooth control over how much sensitive information the model is allowed to use. This design enables: - Transparent fairness–accuracy trade-off - Real-time audit capability - Fully interpretable model components --- ### **Convex Reformulation** FairCoDNet transforms the fairness-constrained QCQP into a convex problem via: - Variable lifting - Coefficient reparameterization - Conic relaxation This guarantees: - Global optimality under mild assumptions - Stable training - Compatible integration with existing convex solvers --- ### **Constraint-Aware Optimization Dynamics (CAOD)** CAOD provides: - Dynamic fairness trajectory monitoring - Adaptive rescaling of sensitive coefficients - Coupled updates of (α, β, γ) - Projection into feasible fairness regions This ensures the model does not drift toward unfair solutions while maintaining predictive performance. --- ## 🧠 Applications FairCoDNet can be applied in various financial and risk-sensitive contexts: - **Enterprise tax-risk identification** - **Credit scoring & loan underwriting** - **Corporate risk modeling** - **Financial QA and legal risk assessment** - **Regulated environments requiring transparency** --- ## 📌 Requirements - Python 3.8+ - PyTorch ≥ 1.10 - NumPy, SciPy - CVXPY (for convex refinement) - Optional: CUDA for acceleration

# GridFormer & InformaNet: Spatio-Informational Intelligence for Power Grid Informatization This repository provides a reference implementation of **GridFormer** and the **InformaNet Policy Layer**, a unified modeling and decision framework for modern power grid informatization. The goal of this project is to bridge **physical grid dynamics**, **information flow**, and **adaptive control** into a single, coherent learning system. The implementation is inspired by the paper *"Big Data Platform Architecture and Information Flow Mechanisms in Power Grid Informatization"* and its proposed graph-based transformer architecture for spatio-temporal reasoning in cyber-physical power systems. --- ## 1. Motivation Modern power grids are evolving into highly digitized, cyber-physical infrastructures. They are: - Geographically distributed - Topologically complex - Continuously monitored by heterogeneous sensors and control devices - Subject to uncertainty from renewables, changing demand, and communication delays Traditional centralized or purely data-driven models: - Struggle to scale to large, dynamic networks - Often ignore the explicit grid topology - Provide limited interpretability for operators - Are not tightly integrated with control and operational constraints This repository focuses on an architecture that explicitly encodes: - The **physical network graph** of the grid - The **information and communication graph** - The **temporal evolution** of system states - A **policy layer** that respects operational feasibility and domain constraints --- ## 2. Core Ideas The project is organized around two major components. ### 2.1 GridFormer GridFormer is a spatio-informational modeling module that: - Represents the grid as a graph of buses and transmission lines - Integrates multimodal node features (physical states, measurements, control inputs) - Uses dual-graph propagation over: - The physical grid topology - The communication / information topology - Employs a temporal transformer to capture long-range time dependencies - Outputs: - Next-step state estimates - Candidate control actions Key design aspects: - Multimodal encoder for fusing physical, informational, and control features - Graph attention layers that propagate messages across the physical and communication graphs - Transformer-based temporal reasoning over a rolling time window - Decoders for state prediction and control proposals ### 2.2 InformaNet Policy Layer The InformaNet policy layer sits on top of GridFormer and transforms raw control proposals into **feasible**, **risk-aware**, and **structurally consistent** actions. It provides: - A **domain-constrained decision manifold**, approximating operational constraints such as voltage bounds and line flow limits - **Spatio-temporal action refinement**, which adjusts actions based on recent trajectories and residual errors - **Graph-based priors**, enforcing smoothness and structural coherence across electrically or topologically close nodes - **Risk-aware diversification**, which uses uncertainty estimates to inject controlled stochasticity for robustness --- ## 3. Conceptual Workflow The typical end-to-end workflow for this project looks like this: 1. **Data ingestion** - Load time-series data of grid states and measurements. - Load or construct graph descriptions of: - Physical grid topology - Communication / information topology 2. **Feature construction** - Build multimodal node features, including: - Physical states (voltages, angles, flows, etc.) - Measurements and sensor readings - Local or regional control-related signals 3. **Spatio-temporal modeling with GridFormer** - Encode node features with the multimodal encoder. - Propagate information across both graphs using dual-graph attention. - Stack a temporal transformer over sliding windows of historical states. - Decode: - Next-step state predictions - Candidate control action proposals 4. **Policy refinement with InformaNet** - Project actions into an approximate feasible set using simple domain-inspired operations. - Refine actions using recent state and prediction history. - Adjust actions with graph-based regularization. - Optionally diversify actions using uncertainty-aware perturbations. 5. **Deployment or simulation** - Use the final actions in: - Power system simulators - Digital twins - Decision-support tools - Log performance metrics such as forecast error, constraint violations, and stability indicators. --- ## 4. Key Features - Explicit handling of **two coupled graphs**: - The physical power network - The information / communication network - Support for **multimodal inputs**, including: - Physical measurements - Control and scheduling signals - High-level system descriptors - **Temporal reasoning** via transformer-style mechanisms for: - Capturing long-term dependencies - Handling variable-length histories - **Policy layer** designed around: - Operational constraints - Structural priors from grid topology - Risk-aware decision diversification - Modular design: - Components can be replaced or extended (for example, using different GNNs, temporal models, or policy mechanisms) --- ## 5. Getting Started High-level steps to use this codebase: 1. Install Python and standard machine learning dependencies (for example PyTorch). 2. Prepare datasets that contain: - Time sequences of node-level features - Physical adjacency matrices for the grid - Communication adjacency matrices (or approximations) 3. Configure model dimensions and hyperparameters. 4. Instantiate the model, construct data loaders, and implement a training loop. 5. Evaluate model performance using application-specific metrics such as: - Prediction error metrics - Rate of constraint violations - Stability or resilience indicators --- ## 6. Potential Use Cases - Short-term state forecasting in power grids - Proactive control and decision support for grid operators - Analysis of information flow and its impact on operational robustness - Research on cyber-physical system integration in smart grids - Educational demos on graph-based and transformer-based models for infrastructure systems --- ## 7. Disclaimer This repository provides a **research-oriented reference implementation**. It is not certified for real-time operation in critical infrastructure. Any deployment in real-world grids must include additional validation, safety layers, and compliance with local regulations and operational standards.

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