arXiv Machine Learning July 14, 2026 By Ryan Gillespie

Byzantine Accountability Without Consensus: Strong Eventual Consistency for Non-Associative, Stochastic, Robust Aggregation

Read the original on arXiv Machine Learning →

arXiv:2607. 10305v1 Announce Type: cross Abstract: Byzantine-robust aggregation rules such as multi-Krum assume a central coordinator, and decentralising them is obstructed by the rules themselves: they are globally coupled, non-associative, and discontinuous, so an ulpscale perturbation can flip the selected subset, moving the output by a non-vanishing amount.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

Jul 20

The Honest Quorum Problem: Epistemic Byzantine Fault Tolerance for Agentic Infrastructure

arXiv:2607. 16109v1 Announce Type: new Abstract: State machine replication (SMR) and Byzantine fault-tolerant (BFT) consensus guarantee agreement despite a bounded number of arbitrary, colluding faulty participants.

By Jun He, Deying Yu

agents safety

arXiv Machine Learning

Jul 10

Secure Decentralized Federated Learning via Gossip and Virtual Voting

arXiv:2607. 08651v1 Announce Type: new Abstract: Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants.

By Amirhossein Taherpour, Xiaodong Wang

efficiency safety

arXiv Machine Learning

Jun 18

Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning

arXiv:2606. 19129v1 Announce Type: cross Abstract: Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem.

By Ousmane Touat, C\'esar Sabater, Mohamed Maouche, Sonia Ben Mokhtar

Hugging Face Trending Papers

Jun 17

Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning

Dealing simultaneously with confidentiality and Byzantine behaviors in decentralized learning is a challenging problem. Indeed, in decentralized learning, clients train a machine learning model while keeping their data locally and share their model parameters or gradients with a set of neighbors.

arXiv Machine Learning

Aug 10

Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning

arXiv:2608. 06469v1 Announce Type: cross Abstract: Collaborative machine learning among financial institutions must be both group-fair and robust against deliberate adversarial manipulation.

By Devharsh Trivedi, Nesrine Kaaniche, Nikos Triandopoulos, Maryline Laurent, Jackson Walters

robotics benchmarks safety

arXiv AI

Jun 16

Trust Without Trusting: A Recomputable Trust Protocol for Autonomous Agents

arXiv:2605. 06738v2 Announce Type: replace-cross Abstract: Autonomous AI agents already transact at production scale -- 69,000 bots, 165 million transactions, $50 million in volume on a single marketplace -- and any party can verify a signed credential without a central service.

By Lars Kersten Kroehl

agents

Related stories

The Honest Quorum Problem: Epistemic Byzantine Fault Tolerance for Agentic Infrastructure

Secure Decentralized Federated Learning via Gossip and Virtual Voting

Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning

Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning

Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning

Trust Without Trusting: A Recomputable Trust Protocol for Autonomous Agents