arXiv AI
OPFL: Optimistic Verification of Federated Learning via Empirical Boundary
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arXiv Machine Learning
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OVIG: Optimistic Verification of AI Training Integrity via Gradient Signals
OVIG is an optimistic verification framework that audits AI training by replaying the process and comparing gradient differences against an empirically calibrated boundary. It treats any gradient difference exceeding this boundary as a malicious deviation. By partitioning training into stride‑s intervals and storing evidence only at interval endpoints, OVIG dramatically reduces off‑chain storage and transmission costs while maintaining zero attack success rate across language, vision, and diffusion workloads.
arXiv Machine Learning
Sep 16
OPEN-1B: A Fully Auditable Training Run
The paper introduces Open-1B, a language model trained under a new fully auditable regime that ensures every training operation is reproducible on heterogeneous commodity hardware with bitwise certainty. By enforcing a fixed order on sources of nondeterminism—GPU reductions, data batch ordering, and inter/intra-node communication—the authors enable auditors to replay and verify individual training steps on a single machine. The release includes the full pretraining dataset, all intermediate checkpoints, the training codebase, and an audit harness for step-by-step verification.
arXiv Machine Learning
Aug 4
FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devices
arXiv:2608. 01095v1 Announce Type: new Abstract: Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data.
Hugging Face Trending Papers
Jun 1
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.
arXiv AI
Jul 9
PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning
arXiv:2607. 06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy.
arXiv Machine Learning
Jun 2
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
arXiv:2606. 02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity.