PFedMA: Who Should Join Next as Federated Multi-Agent Orchestration Evolves?
PFedMA tackles reliable multi-round collaboration in Federated Multi-Agent Systems by jointly adapting agent selection and heterogeneous privacy rules as reasoning unfolds. Its core idea is a closed-loop orchestration that feeds response reliability and capability signals back into an RL-trained router, while a bidirectional local-global mechanism co-evolves privacy rules without overriding personalized constraints. This matters because existing approaches either select agents without historical accountability or enforce static privacy rules that cannot adapt to changing information needs across rounds, leading to error propagation and rigid privacy-utility trade-offs.
The paper presents a conceptually coherent framework that tightly couples agent routing with privacy rule evolution, supported by a broad empirical evaluation. The empirical gains over Fed-ICL and EPEAgents are consistent, yet the lack of formal privacy guarantees and the reliance on a frozen LLM judge for anomaly detection without robustness analysis of the judge itself temper the overall assessment. The work is pragmatic but stops short of the rigorous assurances typically expected in federated privacy research.
The feedback-driven orchestration is well-motivated, and the ablations convincingly isolate the contributions of historical capability states, reliability states, and query refinement. The federated privacy rule co-evolution elegantly resolves the tension between cross-client reuse and personalization by selectively merging global candidates based on local utility scores, while the evaluation spans mathematical reasoning, clinical QA, and adversarial settings with consistent gains.
The primary limitation is the absence of formal privacy guarantees: semantic privacy rules and response filtering do not equate to differential privacy or secure aggregation, leaving exposure bounds undefined.
Second, the entire feedback loop depends on a frozen LLM judge for intent, evidence, and consensus anomaly detection; if this judge is fooled by adversarial responses, unreliable agents may still enter aggregation or propagate biased state updates. RL training additionally requires ground-truth reward signals that may be unavailable in real-world deployments, creating a deployment gap between benchmark evaluation and practical utility.
The comparisons against Fed-ICL, EPEAgents, and centralized MAS baselines are controlled for model pool size and round budget. However, the centralized KABB baseline still outperforms PFedMA on MedQA and MedMCQA, indicating that the federated constraint imposes a residual performance cost even with sophisticated orchestration. Privacy metrics are drawn from established literature, but the adversarial evaluation is limited to prompt injection attacks, leaving open questions about Byzantine agents or colluding clients.
The paper provides detailed pseudocode, hyperparameter settings, and extensive appendices, but no code or data was released at the time of review. Reproduction is further hindered by the need for multiple proprietary and open-weight LLM backbones and substantial GPU resources. Because local datasets, experience buffers, and unsanitized responses remain client-local by design, an independent replicator must reconstruct the full federated client stack, making exact reproduction arduous.
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.
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