arXiv AI
Jun 16
Resilient Consensus in Agentic AI
arXiv:2606. 15024v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed in multi-agent systems where they must coordinate and agree on shared decisions.
Robust Multi-Agent LLMs under Byzantine Faults
arXiv:2605. 09076v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents increasingly collaborate over peer-to-peer networks to improve their reliability.
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arXiv Machine Learning
Sep 23
Fully Byzantine-Resilient Multi-Agent Reinforcement Learning
arXiv:2609.25701v1 Announce Type: new Abstract: We study distributed Byzantine-resilient actor-critic multi-agent reinforcement learning (AC-MARL), where agents collectively learn policies through lo...
arXiv Machine Learning
1d ago
SeedFlood: A Step Toward Scalable Decentralized Fine-Tuning of LLMs
SeedFlood is a novel decentralized fine‑tuning method for large language models that scales to billions of parameters and hundreds of clients. It leverages the seed‑reconstructible structure of zeroth‑order gradients to reduce message sizes to near‑zero, enabling efficient flooding across the network. Experiments show SeedFlood outperforms standard zeroth‑order baselines in communication efficiency and generalization, and rivals first‑order gossip methods while incurring far less communication cost.
arXiv AI
Jun 2
GRANITE : a Byzantine-Resilient Dynamic Gossip Learning Framework
arXiv:2504. 17471v2 Announce Type: replace-cross Abstract: Gossip Learning (GL) is a decentralized learning paradigm where users iteratively exchange and aggregate models with a small set of neighboring peers.
arXiv Machine Learning
Jun 9
Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Games
arXiv:2606. 07790v1 Announce Type: new Abstract: Multi-agent LLM systems increasingly rely on communication protocols for coordination, yet their robustness under adversarial and structural constraints remains poorly understood.
arXiv Machine Learning
Sep 4
A Nesterov-Accelerated Byzantine-Robust Federated Learning
The paper proposes Byrd-NAFL, a Byzantine‑robust federated learning algorithm that incorporates Nesterov’s momentum and resilient aggregation rules. It achieves fast and safe convergence under non‑convex, smooth loss functions with relaxed gradient assumptions, and provides a finite‑time convergence guarantee. Experiments show that Byrd-NAFL outperforms existing methods in convergence speed, accuracy, and resilience to various malicious attacks.
arXiv AI
Sep 2
Optimizing Byzantine Node Placement in Decentralized Federated Learning
The paper investigates how the strategic placement of Byzantine nodes in decentralized federated learning (DFL) affects the propagation of malicious influence across the communication graph. It introduces Byzantine Placement Influence (BPI), a measure that captures cumulative exposure of honest nodes to Byzantine sources over time, and develops algorithms to optimize BPI across various network structures and attack types. Experiments demonstrate that BPI-guided placements consistently yield highly damaging configurations, highlighting the importance of considering node placement in DFL threat models.
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
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.
arXiv AI
Jun 2
Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus
arXiv:2606. 01828v1 Announce Type: cross Abstract: Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specialization, and cross-validation.
arXiv Machine Learning
Sep 10
Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration
The paper introduces a robust decentralized federated distillation approach that allows heterogeneous client models to collaborate using predictions on shared unlabeled public data. Each client evaluates received predictions across three modalities—class prediction, boundary decision, and prediction correlation—filters unreliable clients, assigns reliability-based weights, and constructs modality-specific teachers. The method validates distillation gradients against supervised gradients from private data, removes conflicting gradients, and proves convergence under Byzantine attacks, achieving improved accuracy on CIFAR-10 and CIFAR-100 under non‑IID data and malicious conditions.
Hugging Face Trending Papers
Jun 1
Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus
Large language model-driven multi-agent systems enhance the reliability of complex reasoning tasks through multi-round deliberation, role specialization, and cross-validation. However, existing multi-agent debate and collaboration frameworks typically adopt fully connected communication, causing the number of messages, token costs, and end-to-end latency to grow approximately quadratically with the number of agents; although fixed sparse topologies reduce overhead, they cannot adapt communication relationships to different task instances or intermediate reasoning states, making them prone either to preserving low-value interactions or to losing critical error-correction information.