# FLAIR: Federated Learning with Adaptive and Intelligent Reasoning for Client Selection

Houssem Jmal, Kandaraj Piamrat, Ons Aouedi, Yusheng Ji

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Houssem Jmal, Kandaraj Piamrat, Ons Aouedi, Yusheng Ji. FLAIR: Federated Learning with Adaptive and Intelligent Reasoning for Client Selection. MSWiM 2025 - 27th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Oct 2025, Barcelone (Espagne), Spain. pp.1-5. hal-05288098

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# FLAIR: Federated Learning with Adaptive and
Intelligent Reasoning for Client Selection

Houssem Jmal & Kandaraj Piamrat

Computer Sciences Department

LS2N/INRIA, Nantes University

Nantes, France

firstname.lastname@univ-nantes.fr

Ons Aouedi

SnT, SIGCOM

University of Luxembourg

Luxembourg

ons.aouedi@uni.lu

Yusheng Ji

Information Systems Architecture

Science Research Division

National Institute of Informatics

Tokyo, Japan

kei@nii.ac.jp

**Abstract**—The widespread adoption of the Internet of Things (IoT) in our technology-driven society raises significant security and privacy concerns, highlighting the need for collaborative Intrusion Detection Systems (IDS) that leverage Deep Learning (DL) methods to detect suspicious network traffic. Using both the computing power and local data available at distributed end devices, Federated Learning (FL) provides a decentralized learning paradigm that preserves data privacy. However, the system heterogeneity and the Independent and Identically Distributed (non-IID) distribution of FL clients' data introduce various challenges that impact training efficiency. To address these issues, we propose an adaptive approach named FLAIR, which leverages a semi-synchronous FL mechanism and a Decision Transformer (DT) to select clients that not only enhance the global model's performance but also reduce communication and computation overhead. DT helps to make client selection decisions by considering both current and historical information, with the model trained offline using various client selection policies. The process begins by generating an offline database, which will be used to train the DT offline. Then, the DT is deployed for online client selection under a semi-synchronous protocol, enabling a fully adaptive FL system. Extensive experiments on IDS datasets show that FLAIR significantly reduces computation and communication time by up to 94% and 93%, respectively, while maintaining comparable classification performance to the baseline models.

**Index Terms**—Federated Learning (FL), Intrusion Detection System (IDS), Decision Transformer (DT), Client Selection.

## I. INTRODUCTION

The rapid growth of urban populations and advances in digital infrastructure have boosted the development of smart cities (SC) [1]. SC rely on diverse Internet of Things (IoT) sensors and devices deployed across distributed environments. However, the rapid integration of such devices introduces security vulnerabilities that can be exploited through cyberattacks, such as denial-of-service (DoS) attacks, unauthorized access, or spoofing. Therefore, Intrusion Detection Systems (IDS) leverage Deep Learning (DL) to monitor IoT traffic and detect suspicious activities. However, IoT data is inherently distributed, and privacy regulations prohibit centralizing raw data, despite DL models requiring large training datasets. To address this, Federated Learning (FL) has been proposed as a solution that allows collaborative model training while preserving data privacy [2]. FL involves data owners independently training local models on their private data and sending only the model

parameters to a server for aggregation. This process is repeated over multiple rounds until a target performance is achieved.

Although FL offers a privacy-preserving distributed learning paradigm, it faces several challenges due to the heterogeneity of the end devices, which arises from both data and system resource variability. For instance, clients can have different data distributions (non-IID), making it crucial to identify those whose data benefits the global model, which is referred to as their statistical utility. Moreover, variations in computational power can lead to prolonged training times in synchronous FL settings or cause convergence issues in alternative FL orchestration methods due to the straggler effect. In addition, during the upload phase, clients may experience interference caused by congested service areas, which can cause the same previous issues. These challenges highlight the importance of considering each client's system utility, which encompasses both computational and communication efficiency. To address these issues, client selection has been introduced in FL as a mechanism to select the most beneficial set of clients according to different objectives [3]. In this context, numerous methods rely on reinforcement learning (RL) and its variants to achieve optimal client selection. However, these methods are trained online, which can result in a prolonged training (many FL rounds are needed before the selection mechanism becomes effective). Moreover, they are sensitive to the number of clients and require retraining the entire system from scratch whenever the client pool changes, and often fail to balance between statistical and system efficiencies.

To address these challenges, we propose FLAIR, an adaptive client selection mechanism that balances the utilities of FL clients. Unlike existing RL-based methods that rely on specific FL environments and online training, FLAIR employs an offline RL approach to improve efficiency and reduce environment dependency while balancing clients' statistical and system performance during FL rounds. Moreover, it leverages historical information to capture long-term utility trends, enabling more consistent client selection. Finally, we evaluated FLAIR against four baselines, showing that it achieves a superior balance between model performance and FL time.

The rest of the paper is organized as follows: Section II surveys the literature. Section III presents our solution. Section IV reports our results and we conclude in Section V.
## II. RELATED WORK

Client selection is a critical mechanism that has been widely used in the literature to address the challenges of heterogeneity in FL. On one hand, many works focused on the statistical similarity and diversity between clients. In CCSF [4], clients are first clustered based on the similarity of their local gradient directions using Spearman's rank correlation. At each FL round, the server selects one representative from these clusters guided by one of the following strategies: the least trained client to ensure fairness, the worst-performing client to accelerate convergence, or the best-performing client to exploit informative updates. Furthermore, the authors in [5] propose FedCorr, where clients are selected based on a Gaussian Process (GP) model that captures the loss correlations between clients. In each selection round, the GP model is updated using the predicted losses. Then, FedCorr iteratively chooses clients that are both individually beneficial and mutually diverse, maximizing expected global loss reduction while minimizing redundancy. Moreover, in DivFL [6], clients are selected by using a greedy submodular maximization algorithm over the gradient space, aiming to choose a subset whose aggregated updates best approximate those of the entire client population. Nevertheless, none of these works consider either the computation or communication efficiency of the clients.

On the other hand, Power-of-Choice [7] samples a random candidate set and then selects a fixed-size subset with the highest local losses to participate in training. Besides, Scout [8] selects clients based on a joint utility function that models both data and resource heterogeneity. Rather than scoring clients individually, Scout evaluates the utility of entire client sets to balance training efficiency and wall-clock time. It employs a greedy search algorithm for the optimal set and integrates a multi-armed bandit strategy to maintain fairness. Building on Oort [9], which selects clients based on utility scores without fully leveraging client-specific behaviors, PyramidFL [10] performs a fine-grained client selection by modeling both statistical utility and system utility at both the server and client levels. In PyramidFL, the server ranks clients using a utility function based on past training feedback and assigns each a personalized configuration while introducing a fraction of unexplored clients for fairness. Although these works offer advanced client selection strategies, they rely on a predefined, fixed number of clients during each training round, which can be impractical in different FL scenarios.

Unlike existing literature, this work introduces a proof-of-concept client selection method that considers both the statistical and system utilities of clients without requiring a fixed, predefined number of participants. Instead, it dynamically adapts client selection based on both current and historical information from previous FL rounds.

## III. PROPOSED SOLUTION: FLAIR

In this work, we adopt a similar setting to our previous work TUNE-FL [11], which consist of multiple edge servers interconnected in such a way that there exists at least one communication path between any pair of servers, while data

owners are linked to the closet edge server, illustrating the semi-decentralized FL (SDFL). Besides, we extend it to a more realistic scenario by incorporating client communication capabilities. In this work, clients are characterized by their dynamic computational capabilities and a time-varying wireless transmission channel. While we assume the downlink to be reliable and stable, the uplink can be significantly perturbed due to co-channel interference caused by other devices in the surrounding environment. Therefore, our objective is to adaptively select a subset of clients that ensures the convergence of the global model while maintaining acceptable upload times. Our solution is called Federated Learning with Adaptive and Intelligent Reasoning for client selection (FLAIR). We provide in Section III-A its overview, and we elaborate on the technical details in Section III-B.

### A. Methodology Overview

Since TUNE-FL effectively addresses the issue of computational heterogeneity by determining an adaptive synchronization time based on the convergence status of all clients, we narrow the client selection criteria to focus solely on statistical and communication efficiencies. To achieve this, we design a Decision Transformer (DT)-based agent [12] that makes client selection decisions with a global view of all clients, independent of the number of servers. The agent leverages both current and historical states, actions, and rewards. We model a sequence of FL rounds as a trajectory $\tau = (\hat{R}_1, S_1, a_1, \hat{R}_2, S_2, a_2, \dots, \hat{R}_T, S_T, a_T)$, where $S_t \in \mathbb{R}^{N \times M}$ denotes the system state at round $t$, capturing the status of $N$ clients each described by $M$ features, $a_t \in 0, 1^N$ is the binary client selection vector, and $\hat{R}_t = \sum_{t'=t}^{T} r_{t'} \in \mathbb{R}$ represents the cumulative future reward (return-to-go). FLAIR consists of two phases: offline training and online selection, as illustrated in fig 1.

In the offline phase, we first execute a variety of selection policies (1) under different FL configurations by varying the number of clients, nodes, and other relevant parameters (2). For each configuration, the FL system is trained using the TUNE-FL method (3), while logging the system state, selected clients, and corresponding reward at each round (4). This process produces an offline database capturing diverse FL trajectories, which is then preprocessed and normalized (5) to serve as input for training the DT offline (6), enabling it to learn effective client selection strategies. During the online phase, FLAIR performs client selection using a centralized logic deployable on any edge server. In each FL round $t$, clients train local models via TUNE-FL (7) and send metadata to the edge server, after which the agent determines which clients should upload their local models for aggregation (8).

### B. Design Details

Here, we describe the different steps to prepare the necessary information for the client selection process.

1) Clients' States: We aim to provide various indicators related to both statistical and communication utility, as well as model divergence and historical behavior for each client
<figure><img src="image_2.png" /><figcaption>Fig. 1: Methodology Workflow</figcaption></figure>

k, while ensuring data privacy. First, we build the statistical utility for client k based on [10] as follows:

$$U_{stat,k}^{(t)} = |D_k^{(t)}| \sqrt{\frac{1}{|D_k^{(t)}|} \sum_{j \in D_k^{(t)}} \text{Loss}(j)^2}, \quad (1)$$

where $D_k^{(t)}$ denotes the enlarged seen data samples in the FL round t, as the number of processed samples depends on the synchronization time allocated for each round.

Furthermore, we characterize the communication utility in terms of the client's upload time. The achievable uplink data rate by the client k at round t is formulated as

$$\lambda_k^{(t)} = \gamma_{k,i} B_i \ln \left( 1 + \frac{h_k^{(t)} p_k}{I_{k,i}^{(t)}} \right), \quad (2)$$

where $\gamma_{k,i}$ denotes the allocated fraction of the total available bandwidth $B_i$ at server i with $\sum_{k \in C_i} \gamma_{k,i} = 1$, $p_k$ is the transmission power, $h_k^{(t)}$ represents the channel gain at round t, while $I_{k,i}^{(t)}$ captures the co-channel interference and noise caused by neighboring wireless devices in other service areas of client k. Let d be the size of the model parameters w. The upload time for sending w to the server i and the communication utility for the client k are calculated as:

$$\text{upload}_{k:i}^{(t)} = \frac{d}{\lambda_k^{(t)}}, \quad U_{\text{com},k}^{(t)} = \frac{1}{\text{upload}_{k:i}^{(t)}}, \quad (3)$$

Finally, we define the state $s_k^{(t)}$ of the client k at round t as:

$$s_k^{(t)} = [U_{\text{stat},k}^{(t)}, \text{Mean}(U_{\text{stat},k}), \text{Euc}(w_k^{(t)}, W^{t-1}), \\ \text{Cos}(w_k^{(t)}, W^{t-1}), U_{\text{com},k}^{(t)}, \text{Mean}(U_{\text{com},k})] \quad (4)$$

where Mean($U_{\text{stat},k}$) and Mean($U_{\text{com},k}$) are the average values over the past round for both statistical and communication utilities, which are maintained across rounds to capture recent contributions. Meanwhile, Euc($w_k^{(t)}, W^{t-1}$) and Cos($w_k^{(t)}, W^{t-1}$) corresponds to the Euclidean distance and cosine similarity between the local model and the received global model, offering insights into the local model drift.

2) FL System State and Reward Function: Given N clients, each represented by a local state vector $s_k^{(t)}$, we construct the FL system state matrix $S_t \in \mathbb{R}^{N \times M}$ by concatenating $s_1^{t \le k \le N}$. Our goal is to transform this matrix into a single global input state vector that is invariant to the ordering of clients. To achieve this, first, for each client state vector, we compute its $\ell_2$ norm $\|s_k^{(t)}\|_2$ and then sort all client vectors in descending order based on their norms. This ensures that the resulting FL state $S_t$ is independent of the clients' order. Next, the sorted matrix $S_t$ is flattened into a single vector. The overall process for constructing the final system state $S_t$ is summarized in Equation (5):

$$S_t = \text{Flatten} \left( \text{Sort}_{\| \cdot \|_2} (s_1^{(t)}, \dots, s_N^{(t)}) \right) \quad (5)$$

The intuition behind sorting is to eliminate permutation ambiguity among clients, ensuring that the system state is invariant to their ordering. Besides, flattening the sorted matrix preserves the representation of all individual client states within a unified vector format. Finally, the reward function is defined in Equation (6) based on two components: the improvement in the global model's F1 score from the previous round and the reduction in total upload time.

$$r_t = \gamma_1 \Delta \text{F1}_t + \gamma_2 \Delta \text{CommTime}_t \quad (6)$$

where $\Delta \text{F1}_t = \text{F1}(W^{(t)}) - \text{F1}(W^{(t-1)})$, $\Delta \text{CommTime}_t = \text{CommTime}(t-1) - \text{CommTime}(t)$, and $\text{CommTime}(t)$ is defined as the maximum upload time among all selected clients in round t. Also, $\gamma_1$ and $\gamma_2$ are tunable weights.

3) Offline Training and Online Inference: We assume that the system can accommodate up to N clients. In scenarios where fewer than N are present, both the flattened system state vector $S_t$ and the action vector $a^t$ are padded with zeros to maintain consistent dimensionality. In addition, we generate attention masks for both vectors to distinguish between valid entries and padding. Lastly, we record the state of various FL configurations under predefined selection policies in a database, normalize this information, and use it to train the DT offline. After training, the online agent requires only the appropriate inputs to perform efficient client selection.
# IV. RESULTS AND ANALYSIS

Here, we present the experimental settings in Sec IV-A and compare FLAIR with baseline methods in Sec IV-B.

## A. Experimental Settings

1) **Datasets:** To evaluate our work, we use two reference IDS datasets. The first one (used for training the DT), IoTID20 [13] is a dataset that includes flow-based and general features tailored to wireless environments, enabling the identification of anomalous behavior across IoT networks. The second dataset (not seen by the agent), IoMT-TrafficData [14], captures real-world network flow traffic from Internet of Medical Things (IoMT) devices.

2) **Baselines:** We compare FLAIR against various client selection baselines, mentioned previously, which demonstrate competitive performance in FL:

* **PyramidFL** [10]: A fine-grained client selection method that improves FL efficiency by jointly considering both data quality and system heterogeneity.

* **DivFL** [6]: Selects a representative subset of clients by leveraging submodular maximization, aiming to preserve overall diversity in the aggregated model.

* **Power-of-Choice** [7]: Prioritizes clients with the highest local training loss for participation.

* **TUNE-FL** [11]: Includes all available clients in each round without any selection criteria.

We note that the first three baselines require a predefined number of participating clients *K* and *E* local epochs.

3) **Offline Training Policies:** We build the offline database by recording the state, action, and reward generated by 7 selection policies. These include selecting all available clients; randomly choosing *K* clients either per edge server or from the global client pool; selecting the top-*K* clients solely based on statistical utility, both per group and globally; and selecting the top-*K* clients according to a joint ranking of statistical and communication utility, again at both group and global levels. For each policy, we vary the total number of clients among {100, 150, 200}, and experiment with both 5 and 10 edge servers. For every configuration, we simulate and record 30 FL rounds using only the IoTID20 dataset.

4) **Simulation Settings:** We use a unified simulation setup summarized in Table I. Computational heterogeneity is simulated via random training interruptions based on client type [11], while non-IID data distributions are generated by partitioning the training set among clients using a Dirichlet distribution *Dir*(*α*). Clients are randomly assigned to edge servers. The detection model is a multi-layer perceptron (MLP) with three hidden layers. For the DT, we use GPT-2 as suggested in [12]. All experiments were conducted on a MacBook Pro with an Apple M3 Pro chip and 36GB of RAM.

## B. Comparative Analysis

**FLAIR balances between wall clock time and model's performance.** We compare test accuracy, computation time, and communication cost for the IoMT datasets (not seen by the DT) in fig 2, using 100 clients across all selection methods.

<table><caption>TABLE I: Experimental Values</caption><thead><tr><td>Parameter</td><td>Value</td></tr></thead><tbody><tr><td>Number of edge servers</td><td>5</td></tr><tr><td>Bandwidth per server B<sub>i</sub></td><td>random(1,5) MHz</td></tr><tr><td>Transmission power p<sub>k</sub></td><td>100 mW</td></tr><tr><td>Channel gain h<sub>k</sub><sup>(t)</sup></td><td>Rayleigh(1/√2)</td></tr><tr><td>Co-channel interference I<sub>k,i</sub><sup>(t)</sup></td><td>N(-90, 10) dbm</td></tr><tr><td>Bandwidth fraction of client γ<sub>k,i</sub></td><td>random(0,1)</td></tr><tr><td>Reward weights γ<sub>1</sub> and γ<sub>2</sub></td><td>2, 1</td></tr><tr><td>Number of participants K</td><td>20% of all clients</td></tr><tr><td>Number of local epochs E</td><td>5</td></tr><tr><td>Non-IID data distributions</td><td>Dir(0.5)</td></tr><tr><td>Maximum number of clients N</td><td>200</td></tr><tr><td>Batch size</td><td>16</td></tr><tr><td>Learning rate</td><td>0.001</td></tr><tr><td>Convergence precision ε</td><td>0.01</td></tr><tr><td>Trajectory length T</td><td>5</td></tr><tr><td>Computation distribution</td><td>60% fast, 20% medium, 20% slow</td></tr><tr><td>Training interruption</td><td>0.02 second</td></tr></tbody></table>

We observe that all methods achieve high accuracy over FL rounds, with only marginal differences. Notably, DivFL and Power-of-Choice reach the highest accuracy, consistent with their design that prioritizes clients based solely on statistical utility. However, by ignoring computation and communication costs, they exhibit noticeable fluctuations across rounds(figs 2b and 2c). In contrast, TUNE-FL aggregates updates from all clients and reduces training time via its adaptive synchronization mechanism, though it suffers from high communication cost due to waiting for the slowest client. PyramidFL, which jointly considers statistical utility and system efficiency, increases computation time because of its time pacer mechanism but effectively reduces communication delays. Finally, FLAIR follows a training strategy similar to TUNE-FL, explaining comparable computation times, while achieving the lowest communication cost. This improvement stems from two factors: the distributed nature of SDFL and the DT, which learns from the offline database to select clients that enhance both model performance and communication efficiency.

**FLAIR can generalize to different client population sizes.** We further compare the selection methods in Table II while varying the number of clients. Our approach, FLAIR, achieves performance comparable to baselines that focus solely on statistical utility, such as DivFL and Power-of-Choice. Furthermore, FLAIR consistently outperforms TUNE-FL across both datasets, indicating that selectively choosing the most beneficial clients enhances TUNE-FL's mechanism while preserving its core advantages, as shown in Figs 2b. Moreover, FLAIR maintains a stable selection time of approximately 1.3 ms, independent of the number of clients, as it relies solely on the inference time of the DT. To further validate and show its generalization, we also evaluated FLAIR with 50 clients, a setting not encountered during offline training.

# V. CONCLUSION

In this work, we introduced FLAIR, an adaptive client selection method designed to tackle the challenges of heterogeneity and system variability in FL. Leveraging the DT, FLAIR balances statistical and system utility, enabling robust client selection across diverse FL settings. By incorporating
<figure><img src="image_3.png" /><figcaption>Fig. 2: Comparison of FL approaches over accuracy and time on the IoMT dataset for 100 clients.</figcaption></figure>

<table><caption>TABLE II: Classification performance on the test set</caption><thead><tr><th>Approach</th><th>Clients</th><th>Acc</th><th>Prec</th><th>Rec</th><th>F1</th><th>Sel(ms)</th></tr></thead><tbody><tr><td colspan="7" style="text-align: center; font-weight: bold;">IoTID20</td></tr><tr><td rowspan="3">PyramidFL</td><td>50</td><td>0.9718</td><td>0.9675</td><td>0.9952</td><td>0.9812</td><td>0.240</td></tr><tr><td>100</td><td>0.9740</td><td>0.9768</td><td>0.9882</td><td>0.9825</td><td>0.578</td></tr><tr><td>200</td><td>0.9657</td><td>0.9762</td><td>0.9773</td><td>0.9768</td><td>1.036</td></tr><tr><td rowspan="3">DivFL</td><td>50</td><td>0.9858</td><td>0.9830</td><td>0.9980</td><td>0.9905</td><td>0.296</td></tr><tr><td>100</td><td>0.9861</td><td>0.9891</td><td>0.9921</td><td>0.9906</td><td>0.711</td></tr><tr><td>200</td><td>0.9883</td><td>0.9886</td><td>0.9956</td><td>0.9921</td><td>1.377</td></tr><tr><td rowspan="3">POWER-OF-CHOICE</td><td>50</td><td>0.9842</td><td>0.9856</td><td>0.9931</td><td>0.9893</td><td>0.251</td></tr><tr><td>100</td><td>0.9791</td><td>0.9944</td><td>0.9736</td><td>0.9888</td><td>0.510</td></tr><tr><td>200</td><td>0.9694</td><td>0.9890</td><td>0.9737</td><td>0.9714</td><td>1.152</td></tr><tr><td rowspan="3">TUNE-FL</td><td>50</td><td>0.9700</td><td>0.9891</td><td>0.9701</td><td>0.9795</td><td>N/A</td></tr><tr><td>100</td><td>0.9741</td><td>0.9782</td><td>0.9869</td><td>0.9825</td><td>N/A</td></tr><tr><td>200</td><td>0.9653</td><td>0.9607</td><td>0.9936</td><td>0.9769</td><td>N/A</td></tr><tr><td rowspan="3">FLAIR (Ours)</td><td>50</td><td>0.9763</td><td>0.9783</td><td>0.9918</td><td>0.9850</td><td>1.346</td></tr><tr><td>100</td><td>0.9722</td><td>0.9659</td><td>0.9966</td><td>0.9790</td><td>1.367</td></tr><tr><td>200</td><td>0.9708</td><td>0.9697</td><td>0.9923</td><td>0.9789</td><td>1.366</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">IoMT-TrafficData</td></tr><tr><td rowspan="3">PyramidFL</td><td>50</td><td>0.9932</td><td>0.9955</td><td>0.9906</td><td>0.9931</td><td>0.240</td></tr><tr><td>100</td><td>0.9882</td><td>0.9861</td><td>0.9898</td><td>0.9880</td><td>0.562</td></tr><tr><td>200</td><td>0.9860</td><td>0.9860</td><td>0.9853</td><td>0.9856</td><td>1.029</td></tr><tr><td rowspan="3">DivFL</td><td>50</td><td>0.9957</td><td>0.9989</td><td>0.9923</td><td>0.9956</td><td>0.294</td></tr><tr><td>100</td><td>0.9956</td><td>0.9965</td><td>0.9945</td><td>0.9955</td><td>0.663</td></tr><tr><td>200</td><td>0.9957</td><td>0.9988</td><td>0.9923</td><td>0.9955</td><td>1.547</td></tr><tr><td rowspan="3">POWER-OF-CHOICE</td><td>50</td><td>0.9950</td><td>0.9993</td><td>0.9904</td><td>0.9948</td><td>0.256</td></tr><tr><td>100</td><td>0.9947</td><td>0.9975</td><td>0.9917</td><td>0.9946</td><td>0.466</td></tr><tr><td>200</td><td>0.9957</td><td>0.9992</td><td>0.9920</td><td>0.9956</td><td>1.124</td></tr><tr><td rowspan="3">TUNE-FL</td><td>50</td><td>0.9721</td><td>0.9997</td><td>0.9432</td><td>0.9706</td><td>N/A</td></tr><tr><td>100</td><td>0.9862</td><td>0.9836</td><td>0.9883</td><td>0.9859</td><td>N/A</td></tr><tr><td>200</td><td>0.9764</td><td>0.9666</td><td>0.9856</td><td>0.9760</td><td>N/A</td></tr><tr><td rowspan="3">FLAIR (Ours)</td><td>50</td><td>0.9885</td><td>0.9851</td><td>0.9915</td><td>0.9883</td><td>1.371</td></tr><tr><td>100</td><td>0.9871</td><td>0.9895</td><td>0.9899</td><td>0.9887</td><td>1.356</td></tr><tr><td>200</td><td>0.9864</td><td>0.9864</td><td>0.9857</td><td>0.9860</td><td>1.362</td></tr></tbody></table>

historical client information, it captures temporal trends and identifies the most beneficial clients, allowing it to generalize effectively to unseen environments and datasets. We demonstrate that FLAIR consistently outperforms existing methods in adaptability and agility while maintaining comparable overall performance. Future work will explore more dynamic FL scenarios, including varying client availability and non-stationary local datasets with shifting data distributions.

ACKNOWLEDGEMENT

This work was supported by the ANR CHIST-ERA project Di4SPDS-Distributed Intelligence for Enhancing Security and Privacy of Decentralised and Distributed Systems.

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