Federated Agent Optimization

Qiang Yang; Zhiqiang Kou; Xueyi Zhang; Dong-Dong Wu; Hanlin Gu; Jing Guo; Yang Liu; Di Jiang

cs.AIOct 1, 2026

Federated Agent Optimization

Authors: Qiang Yang, Zhiqiang Kou, Xueyi Zhang, Dong-Dong Wu, Hanlin Gu, Jing Guo, Yang Liu, Di Jiang, +1 more

Organizations: The Hong Kong Polytechnic University, Hong Kong SAR, China · National University of Singapore, Singapore · The University of Tokyo, Japan · Hong Kong University of Science and Technology, Hong Kong SAR, China · Tengen AI, Hong Kong SAR, China

Abstract

Large language model (LLM) agents increasingly operate in private environments and accumulate valuable experience from task execution, tool use, feedback, and local knowledge. Yet such experience is distributed across organizations and cannot be directly shared because of privacy and proprietary constraints. Conventional federated learning is insufficient for this setting, as agent capabilities extend beyond model parameters to memory, tools, rewards, skills, and structured knowledge. In this paper, we formulate \textbf{Federated Agent Optimization (FAO)}, which studies how distributed agents can collaboratively improve through controlled information exchange while keeping raw data, complete trajectories, and private knowledge local. We define FAO as a multi-objective problem balancing agent utility, privacy leakage, and communication cost, and organize its optimization space across policy, memory, tool use, reward, and structured knowledge and skills. We further characterize how private experience can be abstracted, protected, aggregated, and adapted into transferable capabilities, providing a unified view of how agents can benefit from one another without direct experience sharing. Finally, we identify the key challenges of FAO and outline several promising directions for future research toward trustworthy federated agent systems.

Figures & tables

Panel of Figure 1 — Figure 1: One round of FAO. Clients upload typed updates $\Delta_{k}$ , the coordinator aggregates them into views $G_{k}$ , and each client adapts its view. Utility is measured at $A_{k}^{+}$ , leakage on $\Delta_{k}$ , and communication cost on every message that crosses the line.

Category	Shared update	Main leakage channel	Agent-level methods	Precursors
Policy ( $\Delta^{\pi}$ )	adapter weights or gradients; soft or textual prompts	inversion of gradients; private examples kept in prompts	[ 186 , 187 , 185 , 240 ] , [ 35 , 29 ] ∗	[ 229 , 216 , 170 , 30 , 51 ]
Memory ( $\Delta^{\mathcal{M}}$ )	insights, reasoning abstractions, workflows	private content quoted in text	[ 212 , 109 ] ∗	[ 183 ] , [ 2 , 121 ] ∗
Tool use ( $\Delta^{\mathcal{T}}$ )	tool knowledge as text or typed fields; usage statistics	schemas of internal systems; arguments of calls	[ 200 , 25 ] ∗	—
Reward ( $\Delta^{\mathcal{R}}$ )	parameters of evaluators or preference models; scores	labels and outcomes of individual cases	—	[ 199 , 168 ] , [ 90 ] ∗
Structured knowledge ( $\Delta^{\mathcal{O}}$ )	embeddings of entities and relations; rules; skill patches	entity sets and triples; business processes	[ 75 , 209 ] ∗	[ 31 , 138 , 230 ]

Table 1: Taxonomy of FAO. Agent-level methods federate a component of a language agent, precursors the same kind of component outside an agent. ∗ Preprint.

Type	Shared artifact $z$	Example in identity verification	Federated methods
Experience	pattern distilled from successes and failures	normalize the transliteration of names before the registry lookup	[ 212 , 109 ]
Skill	parameterized procedure	recognize the license, query the registry, compare the legal representative, escalate on mismatch	[ 209 ]
Rule	effect of an action in the environment	three failed one-time codes lock the account for 30 minutes	[ 75 ]
Reward	evaluator or rubric	a dialogue is non-compliant if the agent reveals which field failed the check	[ 199 , 90 ] †
Ontology	alignment of concepts	beneficial owner $\equiv$ ultimate controlling person	[ 31 , 138 ] †

Table 2: Five types of shared knowledge, which differ in function, with a running example. † Precursor that federates the component outside an agent (Table 1 ).

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Symbol	Meaning
$K$ , $k$ , $\mathcal{X}$	number and index of clients; task space shared by all clients
$\mathcal{Q}_{k}$ , $u_{k}(\tau)$	task distribution of client $k$ ; utility of a trajectory
$\mathcal{D}_{k}$ , $n_{k}$	private dataset of client $k$ (tasks, logged trajectories, raw records); its number of tasks
$A_{k}^{0}$ , $A_{k}^{t}$	initial agent of client $k$ ; its agent after round $t$
$A_{k}=(\pi_{k},\mathcal{M}_{k},\mathcal{T}_{k},\mathcal{R}_{k},\mathcal{O}_{k})$	policy, memory, tools, reward, structured knowledge (ontologies, rules, skills)
$\theta_{k}$	parameters of the policy $\pi_{k}$ , continuous or discrete

Appendix

Table 3: Notation.

Method	Block	Shared update	Aggregation
Agent-level methods
FedMABench [ 186 ] , FedGUI [ 185 ]	$\Delta^{\pi}$	adapters of GUI agents based on vision-language models	standard federated algorithms
MobileA3gent [ 187 ]	$\Delta^{\pi}$	models trained on automatically annotated phone usage	averaging weighted by episode and step statistics
Fed-SE ∗ [ 35 ]	$\Delta^{\pi}$	adapters tuned on successful trajectories	unweighted averaging of adapters
FedAgent ∗ [ 29 ]	$\Delta^{\pi}$	parameters trained by reinforcement learning	federated averaging
FedWave [ 240 ]	$\Delta^{\pi}$	role-specific adapters of a multi-stage workflow	federated adapters fused by a shared mixture-of-experts router

Appendix

Table 4: Agent-level FAO methods and selected precursors, as instances of the typed update of Eq. ( 5 ). ∗ Preprint.

Shared type	Policy $\pi_{k}$	Memory $\mathcal{M}_{k}$	Tools $\mathcal{T}_{k}$	Reward $\mathcal{R}_{k}$	Structured knowledge $\mathcal{O}_{k}$
Experience	demonstrations for prompting or fine-tuning	insights stored for retrieval	corrected tool descriptions, known failure cases	positive and negative examples for the evaluator	new rules derived from recurring patterns
Skill	procedures placed in the prompt	procedures retrieved on demand	compositions of tools	checkpoints that a correct trajectory must pass	extension of the skill library
Rule	preconditions of actions stated in the prompt	rules retrieved for similar states	expected effects and failure modes of tool calls	penalties for actions with known bad effects	extension of the rule base
Reward	training signal for policy optimization	filter for what is written to memory	reliability scores of tools	parameters or rubrics of the evaluator	validation of new rules and skills
Ontology	shared vocabulary in prompts	retrieval keys over aligned concepts	mapping of tool arguments to shared concepts	criteria stated over shared concepts	aligned concepts and relations

Appendix

Table 5: How knowledge of each type (rows) can improve each component of the receiving agent (columns). One shared artifact can improve several components.

Explore similar work

FedAgentKE: Federated Semantic Knowledge Evolution for Heterogeneous Agents
Jul 23, 2026Weihao Li, Jun Bai, Ziyang SongFedavg Evolutionary Optimization Methods
McCormick School of Engineering, Northwestern University · School of Computer Science, McGill University · Mila – Quebec AI Institute +1
Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning
Sep 2, 2026Jinxi Yu, Eric Hanchen Jiang, Levina Li +6Federated Graph Learning Federated Learning
University of California, Los Angeles
PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs
Jul 13, 2026Jing Liu, Kun Yang, Yan Wang +5Intelligent Edge Low-Rank Adaptation Framework
College of Future Information Technology, Fudan University, Shanghai 200433, China · Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou 215316, China · Department of Electrical and Computer Engineering, The University of British Columbia, BC V6T 1Z4, Canada +8

Jul 23, 2026, cs.MAJ/K move · Enter open · S save

FedAgentKE: Federated Semantic Knowledge Evolution for Heterogeneous Agents

Weihao Li, Jun Bai, Ziyang Song

McCormick School of Engineering, Northwestern University · School of Computer Science, McGill University · Mila – Quebec AI Institute+1

Fedavg Evolutionary Optimization Methods

Open paper AskPDF arXiv

Large language model (LLM)-based agents increasingly rely on reasoning, tool use, and iterative execution, yet existing agent frameworks still operate largely in isolation. While recent memory-based agent systems improve individual agents through local retrieval and workflow reuse, local experiences remain fragmented across isolated agent frameworks, limiting cross-framework knowledge transfer and collaborative reasoning evolution. We propose FedAgentKE, a lightweight framework for Federated Semantic Knowledge Evolution across heterogeneous agents. FedAgentKE enables distributed agent frameworks to collaboratively evolve transferable reasoning abstractions through iterative semantic knowledge distillation, aggregation, and adaptation without sharing raw reasoning trajectories. Experiments demonstrate consistent improvements under both cross-framework and cross-task settings, highlighting the potential of federated semantic knowledge evolution for future collaborative agent ecosystems.

Abstract

Explore similar work

FedAgentKE: Federated Semantic Knowledge Evolution for Heterogeneous Agents

Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning

PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs