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: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.
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...
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: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: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.
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.