ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery

ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery | Signal Canvas | ScienceToStartup

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Signal Canvas proof surface

Canonical route: /signal-canvas/ares-scalable-and-practical-gradient-inversion-attack-in-federated-learning-through-activation-recovery

stale

Proof freshness: stale
Proof status: partial
Display score: 7/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 50%

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

Agent Handoff

Canonical ID ares-scalable-and-practical-gradient-inversion-attack-in-federated-learning-through-activation-recovery | Route /signal-canvas/ares-scalable-and-practical-gradient-inversion-attack-in-federated-learning-through-activation-recovery

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/ares-scalable-and-practical-gradient-inversion-attack-in-federated-learning-through-activation-recovery

MCP example

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  }
}

source_context

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Evidence Receipt

Route status: building

Claims: 0

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery

PDF: https://arxiv.org/pdf/2603.17623v1

Repository: https://github.com/gongzir1/ARES

Source count: Pending verification

Coverage: 50%

Last proof check: 2026-03-19T21:58:08.528Z

Signal Canvas receipt window

Ready for execution: ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery

/buildability/ares-scalable-and-practical-gradient-inversion-attack-in-federated-learning-through-activation-recovery

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Subject: ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery

Verdict

ARES: Scalable and Practical Gradient Inversion Attack in Federated Learning through Activation Recovery

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