Add ASCII art figures to all 6 generated Internet-Drafts
Each draft gets 2 illustrative figures: - ABVP: architecture components + verification workflow - ATD: example DAG structure + execution state transitions - HITL: primitive framework overview + approval workflow sequence - AEM/PPALP: federated learning architecture + aggregation flow - RARP: cross-domain architecture + two-phase rollback protocol - APAE: layered architecture + cross-domain provenance tracking Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -337,6 +337,29 @@ Table of Contents
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architectural levels to provide defense-in-depth protection
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against information leakage.
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Tenant A Tenant B Tenant C
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+--------+ +--------+ +--------+
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| Agent | | Agent | | Agent |
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| Local | | Local | | Local |
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| Model | | Model | | Model |
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+---+----+ +---+----+ +---+----+
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| | |
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v v v
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+---+----+ +---+----+ +---+----+
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| DP | | DP | | DP |
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| Noise | | Noise | | Noise |
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+---+----+ +---+----+ +---+----+
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| | |
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+-------+-------+-------+------+
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| |
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+----+-----+ +----+------+
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| Secure | | Privacy |
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| Aggre- | | Compliance|
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| gation | | Verifier |
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+----------+ +-----------+
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Figure 1: PPALP Federated Learning Architecture
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The PPALP architecture defines four primary component roles that
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collectively ensure privacy-preserving federated learning
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operations. The Federated Coordinator serves as the central
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@@ -471,6 +494,23 @@ Table of Contents
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computational efficiency suitable for practical federated learning
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deployments.
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Agent_i Aggregator Verifier
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| | |
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|--local grad--->| |
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| + DP noise | |
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| [...N agents contribute...] |
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| |--aggregate---->|
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| | model update |
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| | |
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| |<--ZK proof-----|
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| | compliance |
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|<--global upd---| |
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Figure 2: Privacy-Preserving Aggregation Flow
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The Multi-Tenant Federated Averaging mechanism serves as the
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foundation for secure parameter aggregation. Each tenant's Privacy
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Compliance Verifier MUST encrypt their differentially-private
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