Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training

Deepthy K. Bhaskar; VP Binu; B Minimol

cs.MASep 28, 2026

Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training

Authors: Deepthy K. Bhaskar, VP Binu, B Minimol

Organizations: Department of Computer Engineering, Model Engineering College, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, Kerala, India. · Department of Biomedical Engineering, Model Engineering College, APJ Abdul Kalam Technological University, Thiruvananthapuram 695016, Kerala, India.

Abstract

Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it suitable for privacy-sensitive applications such as healthcare, finance, and edge intelligence. However, conventional FL approaches rely on static client participation and fixed aggregation strategies, which limits their effectiveness under non-IID data distributions, heterogeneous client behavior, and noisy or unreliable updates. To overcome these issuess, this paper proposes an Agentic Federated Learning (AFL) framework that integrates lightweight rule-based autonomous agents at both client and server levels. The proposed framework introduces a Client-Side Agent (CSA) that dynamically adapts local training parameters, controls participa- tion, and evaluates update reliability, while a Server-Side Orchestrator Agent (SSOA) performs quality-aware client selection and adaptive aggregation. Unlike traditional FL methods, AFL enables context-aware decision-making during the training process, improving adaptability and robustness in dynamic distributed environments. Extensive experiments conducted on the CIFAR-10 dataset under IID, non-IID, and noisy-client settings demonstrate that AFL consistently outperforms standard base- lines including FedAvg and FedProx. Experimental results show improvements in classification accuracy, convergence speed, robustness against corrupted updates, and communication efficiency. Ablation studies further confirm the complementary contributions of CSA and SSOA, while statistical analysis validates the significance of the observed gains. The proposed AFL framework demonstrates that incorporating autonomous agentic reasoning into federated learning provides an effective and practical solution for intelligent, adaptive, and robust distributed learning systems.

Figures & tables

Figure 1 : Architecture of the proposed Agentic Federated Learning framework consisting of Client-Side Agents and Server-Side Orchestrator Agent.

Symbol	Description
$K$	Total number of participating clients
$D_{k}$	Local dataset of client $k$
$w$	Global model parameters
$w_{k}$	Local client model parameters
$F(w)$	Global optimization objective
$F_{k}(w)$	Local objective function of client $k$

Table 1 : Notation and symbol definitions

w^{(t+1)}=w^{(t)}-\sum_{k\in S_{t}}\alpha_{k}^{(t)}\Delta w_{k}^{(t)}

Algorithm 1 Agentic Federated Learning (AFL)

Method	IID	Non-IID	Noisy Clients
FedAvg	78.5	64.2	52.7
FedProx	79.1	66.8	55.3
AFL-CSA	80.3	70.1	58.4
AFL-SSOA	81.0	71.4	60.2
Full AFL	83.2	74.9	63.8

Table 2 : Classification accuracy (%) comparison under different federated learning settings

Panel of Figure 2 — Figure 2 : Convergence comparison of FedAvg, FedProx, and AFL under different federated learning environments.

Method	Accuracy (%)
FedAvg	52.7
FedProx	55.3
AFL-CSA	58.4
AFL-SSOA	60.2
Full AFL	63.8

Table 3 : Accuracy (%) under noisy-client environments

Figure 3 : Communication cost comparison across federated learning methods.

Method	Communication Reduction
FedAvg	Baseline
FedProx	6%
Full AFL	18%

Table 4 : Communication reduction comparison

Configuration	IID	Non-IID	Noisy Clients
FedAvg	78.5	64.2	52.7
FedProx	79.1	66.8	55.3
AFL-CSA Only	80.3	70.1	58.4
AFL-SSOA Only	81.0	71.4	60.2
Full AFL	83.2	74.9	63.8

Table 5 : Ablation analysis of AFL components

Comparison	$p$ -value	Significance
AFL vs FedAvg (IID)	0.031	Significant
AFL vs FedAvg (Non-IID)	0.008	Significant
AFL vs FedAvg (Noisy)	0.004	Significant
AFL vs FedProx (Non-IID)	0.012	Significant

Table 6 : Statistical significance analysis using paired t-test

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Federated learning (FL) is a decentralized approach that enables collaborative model training without exposing raw data. Instead of transferring sensitive data, it allows devices to share only model weights, keeping personal data locally and secure. However, in real world settings, the data held by devices is often not evenly distributed and devices mostly differ in computing power and memory capacity. These differences make FL harder to maintain consistent performance across the system. To address these issues, we propose FedMTFI, a novel architecture that combines multi-teacher knowledge distillation (MTKD) with feature importance to improve the FL process in heterogeneous environments. In FedMTFI, clients are clustered based on similar hardware and model types. Each cluster trains a specific model on not independently and identically distributed (non-IID) data. Within a cluster, every client updates that model using only its own local private data. The server then aggregates the locally trained models in each cluster using FedAvg to form multiple prototype models. Then these prototypes serve as teacher models to train a global generalized student model using MTKD. What makes FedMTFI more unique is the integration of Shapley values (SHAP) to emphasize important features during distillation, which enhances both accuracy and interpretability. Experimental results show that FedMTFI achieves higher accuracy than traditional FL algorithms and performs more effectively under non-IID data conditions.

Abstract

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