ARXIV:2602.16109 · INSIDER THREAT DETECTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

Q: What products could be built from this research?

Develop FedGraph-AGI as a subscription-based software solution for government bodies, offering advanced threat detection capabilities tailored for financial networks.

Q: What are the practical use cases?

A financial regulatory body could use FedGraph-AGI to monitor and detect suspicious activities in cross-border transactions, alerting authorities to potential insider threats.

Q: What industries could this research disrupt?

FedGraph-AGI replaces traditional rule-based and supervised learning approaches that cannot handle multi-jurisdictional datasets due to privacy constraints.

arXiv

FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights. Existing approaches face fundamental limitations: they cannot effectively share intelligence across borders due to privacy constraints, lack reasoning capabilities to understand…

METHOD

Full abstract

Cross-border insider threats pose a critical challenge to government financial schemes, particularly when dealing with distributed, privacy-sensitive data across multiple jurisdictions. Existing approaches face fundamental limitations: they cannot effectively share intelligence across borders due to privacy constraints, lack reasoning capabilities to understand complex multi-step attack patterns, and fail to capture intricate graph-structured relationships in financial networks. We introduce FedGraph-AGI, a novel federated learning framework integrating Artificial General Intelligence (AGI) reasoning with graph neural networks for privacy-preserving cross-border insider threat detection. Our approach combines: (1) federated graph neural networks preserving data sovereignty; (2) Mixture-of-Experts (MoE) aggregation for heterogeneous jurisdictions; and (3) AGI-powered reasoning via Large Action Models (LAM) performing causal inference over graph data. Through experiments on a 50,000-transaction dataset across 10 jurisdictions, FedGraph-AGI achieves 92.3% accuracy, significantly outperforming federated baselines (86.1%) and centralized approaches (84.7%). Our ablation studies reveal AGI reasoning contributes 6.8% improvement, while MoE adds 4.4%. The system maintains epsilon = 1.0 differential privacy while achieving near-optimal performance and scales efficiently to 50+ clients. This represents the first integration of AGI reasoning with federated graph learning for insider threat detection, opening new directions for privacy-preserving cross-border intelligence sharing.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through experiments on a 50,000-transaction dataset across 10 jurisdictions, FedGraph-AGI achieves 92.3% accuracy, significantly outperforming federated baselines (86.1%) and centralized approaches (84.7%).

WHY NOW

Insider Threat Detection moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainFedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.16109 · INSIDER THREAT DETECTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

arXiv

FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Insider Threat Detection moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainFedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.16109

Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback32 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Insider Threat Detection moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights. Existing approaches face fundamental limitations: they cannot effectively share intelligence across borders due to privacy constraints, lack reasoning capabilities to understand complex multi-step attack patterns, and fail to capture intricate graph-structured relationships in financial networks.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Cross-border insider threats pose a critical challenge to government financial schemes, particularly when dealing with distributed, privacy-sensitive data across multiple jurisdictions. Existing approaches face fundamental limitations: they cannot effectively share intelligence across borders due to privacy constraints, lack reasoning capabilities to understand complex multi-step attack patterns, and fail to capture intricate graph-structured relationships in financial networks.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Through experiments on a 50,000-transaction dataset across 10 jurisdictions, FedGraph-AGI achieves 92.3% accuracy, significantly outperforming federated baselines (86.1%) and centralized approaches (84.7%).
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Insider Threat Detection moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Insider Threat Detection

Competitors

not indexed

Competitive landscape

FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.

Segment

Insider Threat Detection

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.16109 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(32)

Large Action Models: From Inception to Implementation

2024Lu Wang, Fangkai Yang et al.

Stochastic Controlled Averaging for Federated Learning with Communication Compression

2023Xinmeng Huang, Ping Li et al.

Unifying Large Language Models and Knowledge Graphs: A Roadmap

2023Shirui Pan, Linhao Luo et al.

GPT-4 Technical Report

2023OpenAI Josh Achiam, Steven Adler et al.

Alleviating Structural Distribution Shift in Graph Anomaly Detection

2023Yuan Gao, Xiang Wang et al.

RT-1: Robotics Transformer for Real-World Control at Scale

2022Anthony Brohan, Noah Brown et al.

GLaM: Efficient Scaling of Language Models with Mixture-of-Experts

2021Nan Du, Yanping Huang et al.

FedGraph: Federated Graph Learning With Intelligent Sampling

2021Fahao Chen, Peng Li et al.

On the Opportunities and Risks of Foundation Models

2021Rishi Bommasani, Drew A. Hudson et al.

Subgraph Federated Learning with Missing Neighbor Generation

2021Ke Zhang, Carl Yang et al.

Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection

2021Yang Liu, Xiang Ao et al.

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

2021W. Fedus, Barret Zoph et al.

A Generalization of Transformer Networks to Graphs

2020Vijay Prakash Dwivedi, X. Bresson

Improved TrAdaBoost and its Application to Transaction Fraud Detection

2020Lutao Zheng, Guanjun Liu et al.

Adaptive Federated Optimization

2020Sashank J. Reddi, Zachary B. Charles et al.

Federated Optimization in Heterogeneous Networks

2018Anit Kumar Sahu, Tian Li et al.

Scalable Graph Learning for Anti-Money Laundering: A First Look

2018Mark Weber, Jie Chen et al.

Insider Threat Detection with Deep Neural Network

2018Fangfang Yuan, Yanan Cao et al.

Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

2018Dong Yin, Yudong Chen et al.

Detecting and Preventing Cyber Insider Threats: A Survey

2018Liu Liu, O. De Vel et al.

Showing 20 of 32 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkHide and Find: A Distributed Adversarial Attack on Federated Graph Learning

7.0

Prior WorkUniAI-GraphRAG: Synergizing Ontology-Guided Extraction, Multi-Dimensional Clustering, and Dual-Channel Fusion for Robust Multi-Hop Reasoning

7.0

Prior WorkMulti-Scale Adaptive Neighborhood Awareness Transformer For Graph Fraud Detection

7.0

Extension

Builds On ThisCognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems

0.0

Builds On ThisL2IR: Revealing Latent Intent in Graph Fraud Detection

0.0

Builds On ThisSupercharging Federated Intelligence Retrieval

4.0

Builds On ThisBeyond RAG for Cyber Threat Intelligence: A Systematic Evaluation of Graph-Based and Agentic Retrieval

4.0

Builds On ThisIntegrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval

2.0

Commercially relevant

Higher ViabilityLegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning

8.0

Higher ViabilityTA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection

8.0

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.16109
Route: /paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes
Active tab: read
Artifact: federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes/paper-pack
REST build passport API/api/v1/paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "6ccb814e-848a-43be-ab30-86fd7a102834",
  "arxiv_id": "2602.16109",
  "canonical_route": "/paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes",
  "endpoints": {
    "paper_pack": "/api/v1/paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes/paper-pack",
    "build_passport": "/api/v1/paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

Canonical ID federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes | Route /paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.16109"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes",
  "normalized_query": "2602.16109",
  "route": "/paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes",
  "paper_ref": "federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

/buildability/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes

Watchwatch

Subject: Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes

Paper ref

federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes

arXiv id

2602.16109

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

12f5f70116478299ffc23935f58e238c14f6519c634c65e529e1c48255a4d32d

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

{
  "@context": "https://schema.org",
  "@graph": [
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      "url": "https://sciencetostartup.com/paper/federated-graph-agi-for-cross-border-insider-threat-intelligence-in-government-financial-schemes",
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      "description": "FedGraph-AGI enhances insider threat detection with federated, privacy-preserving graph learning using AGI insights.",
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Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

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