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This is an arXiv preprint proposing PFedMA, a federated framework for LLM-based agents that selects participants based on historical reliability, validates responses before aggregation, and evolves privacy rules across clients. The reported gains in task performance, privacy leakage, and robustness are the authors' own results, so readers should check the benchmarks, threat models, and baselines before treating them as established. For those building privacy-preserving multi-agent systems, the notable idea is treating privacy rules as something that evolves across rounds rather than as fixed policy.
PFedMA: Who Should Join Next as Federated Multi-Agent Orchestration Evolves?
arXiv:2610.19458v1 Announce Type: new Abstract: Federated Multi-Agent Systems (FedMAS) offer a promising paradigm for collaborative reasoning among LLM-based agents while keeping raw client data decentralized. However, reliable multi-round collaboration remains challenging. Agent selection must take into consideration evolving task requirements and the agent's historical reliability, while privacy rules must adapt to changing collaboration contexts and meet the client's personalized needs. To address these challenges, we propose PFedMA, a federated multi-agent framework for feedback driven multi-round collaboration under heterogeneous privacy requirements. Across rounds, PFedMA maintains historical capability and reliability states to guide agent selection, evaluates task responses before aggregation, and refines subsequent queries according to accumulated collaboration feedback. PFedMA enables bidirectional local to global and global to local private rule evolution across heterogeneous clients. Clients generalize locally updated privacy rules and selectively incorporate globally maintained rules, thereby facilitating cross-client privacy rule evolution while preserving personalized constraints. Extensive experiments across reasoning, privacy sensitive, and adversarial settings show that PFedMA improves task performance, reduces measured privacy leakage, and improves system robustness.
Title:PFedMA: Who Should Join Next as Federated Multi-Agent Orchestration Evolves?
Abstract:Federated Multi-Agent Systems (FedMAS) offer a promising paradigm for collaborative reasoning among LLM-based agents while keeping raw client data decentralized. However, reliable multi-round collaboration remains challenging. Agent selection must take into consideration evolving task requirements and the agent's historical reliability, while privacy rules must adapt to changing collaboration contexts and meet the client's personalized needs. To address these challenges, we propose PFedMA, a federated multi-agent framework for feedback driven multi-round collaboration under heterogeneous privacy requirements. Across rounds, PFedMA maintains historical capability and reliability states to guide agent selection, evaluates task responses before aggregation, and refines subsequent queries according to accumulated collaboration feedback. PFedMA enables bidirectional local to global and global to local private rule evolution across heterogeneous clients. Clients generalize locally updated privacy rules and selectively incorporate globally maintained rules, thereby facilitating cross-client privacy rule evolution while preserving personalized constraints. Extensive experiments across reasoning, privacy sensitive, and adversarial settings show that PFedMA improves task performance, reduces measured privacy leakage, and improves system robustness.
Artificial Intelligence (cs.AI)
arXiv:2610.19458 [cs.AI]
(or arXiv:2610.19458v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.19458 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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