Rewarding the Rare: Maverick-Aware Shapley Valuation in Federated Learning
Authors: Mengwei Yang
Baturalp Buyukates
Athina Markopoulou
Venue: Accepted by TMLR ()

Abstract: Federated Learning (FL) allows clients to train a model collaboratively without sharing their private data. Shapley value (SV) provides a principled way to quantify client contributions in FL. However, existing SV methods use uniform per-class weighting during validation, treating all classes as equally important. This uniform weighting breaks down in the presence of clients with underrepresented or rare classes, also referred to as Mavericks. Such clients are often undervalued due to lower model performance on these challenging classes, despite their critical role in improving generalization. To address this, we introduce a Maverick-aware Shapley valuation framework that reweights validation scores based on per-class accuracy, assigning greater importance to classes where models perform poorly. Building on this, we design FedMS, a Maverick-Shapley client selection mechanism that leverages our refined contribution scores to guide intelligent client selection. Experiments on benchmark datasets demonstrate that FedMS improves model performance and better recognizes valuable client contributions, even under scenarios involving adversaries, free-riders, and skewed or rare-class distributions.

Published in Transactions on Machine Learning Research (11/2025)
Rewarding the Rare: Maverick-Aware Shapley Valuation in Federated Learning
Mengwei Yang
menguey(ruiieeuv
Deportment of Electrical Engineering and Computer Science University of California, Irvine
Baturalp Buyukates
b. www.nnkkatssbbaaaaaa.
School Computer Science
University of Birmingham
Athina Markopoulou
athina@uci edu
Department of Electrical Engineering and Computer Science University of California, Ireine
Reviewed on OpenReview: https: openreevee. net/ forum? Jtyb
Abstract
Federated Learning (FL) allows clients to train a model collaboratively without sharing their private data. Shapley value (SV) provides principled way to quantify client contributions in FL. However, existing SV methods use uniform per-class weighting during validation, treating all classes equally important. This uniform weighting breaks down in the presence of clients with underrepresented rare classes, also referred to Mavericks. Such clients are often undervalued due to lower model performance on these challenging classes, despite their critical role in improving generalization. To address this, we introduce a Maverick-aware Shapley valuation framework that reweights validation scores based on per-class accuracy, assigning greater importance classes where models perform poorly. Building on this, we design FedMS, Maverick- Shapley client selection mechanism that leverages our refined contribution scores to guide intelligent client selection. Experiments benchmark datasets demonstrate that FedMS improves model performance and better recognizes valuable client contributions, even under scenarios involving adversaries, free-riders, and skewed or rare- class distributions.
1 Introduction
Federated Learning (FL) enables collaborative model training across distributed clients without requiring direct access to raw data (McMahan et al. 2017). Clients commmicate only local model updates with a central server, keeping their data on-device and thereby enhancing privacy. While this distributed setup offers privacy advantages, also introduces significant challenges in understanding and leveraging the role of each client in the learning process.
A key challenge in FL is how to evaluate client contributions accurately. Shapley value (SV) has become widely adopted tool for ovaluating client contributions based on their marginal impact on model performance. In practice, validation accuracy is commonly used as the utility function for SV estimation, as it provides an effective and interpretable measure of model quality across clients and rounds. However, SV estimation in FL typically aggregates validation accuracy uniformly across all classes, implicitly assuming that each class is equally important, equally frequent, and equally casy to learn. Such an assumption fails in settings where data is imbalanced, particularly in the presence of clients holding
underrepresented classes.

---

Published in Transactions on Machine Learning Research (11/2025)
These clients, referred to Mavericks (Huang et al. 2022 hold rare data in FL, typically consisting of one or more classes that they exclusively own and that are absent from other clients. Mavericks play vital role in improving model generalization. Incorporating such clients helps mitigate algorithmic bias, leading to more robust and trustworthy machine learning systems. For example, when training an FL model for a disease classification lask (Song et al. 2024 Chen et al., 2023), most hospitals (í.., clients) can possess data indicating common diseases such as flu or other frequent infoctions. However, few hospitals may possess more rare disease datasets such as for leukemiaa or thyroid cancers, making them Mavericks for this learning task. Another example of Mavericks is people with rare accents for training voice-activated AI systems like Amazon's 's Alexa and Google's s Home Assistant. While the majority of these devices contain native accent data, few of them contain data from users with non-native accents. Recent studies (Kamegne et al. 2025 Michel et al., 2025) report that these voice systems struggle to understand non-native accents. This performance disparity indicates a biased performance and demonstrates the importance of training with rare (or less common) data from Mavericks, create models that 'spcak" to everyone.
Despite their importance, Mavericks are systematically undervalued by existing SV-based methods. Rare classes are often harder to learn and tend to yield lower validation accuracy, causing clients that contribute such data to appear less useful when uniform class weighting is applied during evaluation. As a result, SV assigns disproportionately low contribution scores to Mavericks. This misvaluation can lead to biased global models, suboptimal training dynamics, and disincentivized participation from Mavericks whose data is both rare and valuable.
In this paper, we propose a principled reweighting of the validation score used in SV estimation, incorporating class difficulty to enable more accurate and equitable assessment of client contributions. Specifically, we introduce class-aware validation strategy that places greater emphasis on performance over rare and hard- to-learn classes. This reweighting mitigates the bias introduced by uniform validation and more faithfully captures the true utility of each client, particularly Mavericks, during training.
Building on this reweighted valuation, we introduce FedMS, a Maverick-aware client selection mechanism that leverages the refined Shapley scores to guide participation each round. In contrast. to prior selection strategies, FedMS adaptively prioritizes clients based on the difficulty and rarity of the classes they contribute, enabling more effective utilization of Mavericks and most valuable participants. This leads to improved performance and robustness, particularly in the presence of Mavericks in FI.
In this paper, we offer a principled approach for valuing and leveraging Mavericks who contribute rare or hard-to-learn classes in FL. Our key contributions are as follows:
We identify a key limitation in existing SV-based methods, showing that uniformly weighted vali- dation scores systematically undervalue clients with rare or underropresented classes, thus limiting both contribution estimation and model generalization.
We propose Maverick-Shapley, Maverick-aware Shapley valuation mechanism that reweights per- class validation scores based on empirical class difficulty, leading to more accurate and robust con- tribution assessments.
We develop FedMS, a contribation-guiieed client selection strategy that leverages Maverick-Shaple to prioritize and effectively utilize valuable clients, including Mavericks, even in adversarial and free-rider settings.
2
Related Work
Contribution Evaluation via Gradient Shapley Methods. SV, classic tool from cooperative game theory, has been widely adopted in FL to quantify client contributions based on their marginal impact on model performance. However, exact SV computation infeasible in FL due to the need to retrain models over all possible client permutations. To address this, a line of work has focused on gradient-based SV approximations that avoid costly retraining by leveraging clients' gradient updates. Reference (Song et al. 2019) introduces two SV estimators: One-Round (OR), which computes SV post-training, and Multi-Round
2

---

Published in Transactions on Machine Learning Research (11/2025)
(MR), which updates SV estimates throughout training. Truncated Multi-Rounde Construction (TMR) Wei
et al. 2020) improves efficiency by truncating unnecessary sub-model evaluations with a decay factor. Guided Truncation Gradient Shapley (GTG) (Liu et al., 2022) further refines this process by combining guided Monte Carlo sampling with gradient-based truncation. Despite these advances, existing SV approximations such as OR, MR, TMR, and GTG commonly rely on uniformly weighted validation accuracy as the utility function, making them ineffective in the presence of rare or underrepresented classes. Prior work (Huang et al., 2022; Buyukates et al., 2023) has shown that such methods systematically undervalue Mavericks, as their lower accuracy on rare classes leads to reduced contribution scores under uniform weighting. In this work, we address this fundamental limitation by proposing Maverick-aware SV framework that reweights per-class validation scores according to class difficulty, enabling more accurate valuation of Mavericks. Although both CS-Shapley (Schoch et. al., (022) and ShapFed (Tastan et al. 2024) incorporate class-wise SV, their goals differ from ours. CS- Shapley decomposes instance contributions into in-class and out-of-class accuracy for attribution centralized ML, while ShapFed uses class-wisc SV to construct a weighted aggregation schome in FL. In contrast, our FedMS explicitly largets Mavericks with rare data distributions, recognizing their rare class contributions to enhance client selection in FL, which CS-Shapley and ShapFed has not considered.
Client Selection in the Presence of Mavericks. In FL., Mavericks are typically underrepresented in the broader client population. Although they are crucial for improving model generalization, these clients are often overlooked or underutilized in standard FL, training. The FedAvg algorithm (McMahan et al. 2017), which randomly samples clients in each round, does not prioritize those contributing rare or criti- cal information (Fu et al.) 20023). Several client selection strategies have been developed to address data heterogeneity. S-FedAvg (Nggllapatti & Narayanam 2021) combines FedAvg with SV estimation to favor clients with higher estimated contributions. GroedyFed Fed (Singhal et al., 2024) selects clients using fast SV approximation method known as GTG (Liu et al. 2022) Power of Choice (PoC) (Cho et al.) 2022] sched- ules clients with the highest local loss in each round. However, none of these methods explicitly identifies or prioritizes Mavericks, nor do they effectively capture the contribution of clients with rare data. FedEMD (Huang et al.) 2022) is one of the few methods that explicitly increases the selection probability of Maver- icks, using the Wasserstein distance between local and global class distributions. While this improves their sampling frequency, it does not provide a principled mechanism to quantify their actual contributions during training. On the other hand, we develop a Maverick-aware Shapley client valuation mechanism and leverage it to guide client selection in each training round in the presence of clients with rare data. By priorilizing clients based on the rarity and difficulty of the classes they contribute, our method enables more effective utilization of Mavericks across diverse data settings.
Data Heterogeneity in FL. FL suffers from performance degradation under heterogeneous client data. To address this, numerous works have studied different types of heterogencity, including covariate shift (where the feature distribution varies) and concept shift (where class distributions differ). For instance, FedBN [Li et al.) 2020b) employs local batch normalization to alleviate feature shift, while IFCA (Ghosh et all 2020) and FedDrift (Jothimurugesan et al., 2023) partition clients into clusters to address concept shift. Personalized FL approaches such as FedALA (Zhang et al. 2023) and meta-learning methods like (Fallah) et al.) 2020) aim to adapt models to diverse client objectives. Our setting, referred to as the Maverick scenario, can be seen as an extreme case of concept shift, where certain clients exclusively hold one or more classes that are entirely absent from the data of other clients. This creates sharp class-wist imbalances that challenge the standard evaluation and aggregation strategies in FL.
3 Problem Setup and Background
In this section, we first formalize the FL framework (McMahan et al. 2017) and define Mavericks (Huang et al. 2022). We then give an overview of the SV-based methods for evaluating the contribution of clients. Figure iilustrates the data distribution among clients across three Mavericks scenarios we consider in this work.
Federated Learning (FL). We consider a standard FL setup consisting of central server and a set of clients K. = 11,,..... I). Each client i 1C holds a local dataset. D, of size n, = |Dzy]- The total number
3

---

Published in Transactions on Machine Learning Research (11/2025)
20
40
10
45
35
CDientID
Cliet ID
CleetiD
(a) Mavericks + Di0
(b) Mavericks + Dir(1)
(c) Mavericks + Dir(0.1)
Figure 1: Data sample distribution across clients in three Mavericks scenarios for MNIST and CIFAR-10 datasots. Mavericks are shown as blue circles, and non-Mavericks as red. Cirele size indicates sample count, with heterogeneity controlled by y in Dir(t). Higher 7 means lower heterogeneity. (a) the most IID setting with identical distributions for all but Mavericks. (b) shows moderate heterogeneity, and (c) is the most Non-IID. In all cases, the Mavericks exclusively hold certain class labels (e.g., class 5 and class 8), while still containing samples from other classes.
of samples across all clients is Esex n4. Each data point is pair (a, v), where denotes the feature vector and M (1.2....,C) C) is the corresponding class label. Let w denote the parameters of the global model. The objective of FL is to minimize the weighted average of local losses:
min L(w) =
Σ iEK
T4
L,(w),
(1)
n
where the local loss at client is defined as:
(((w) Th (m.D))
1
Σ
E(w; x. v),
(2)
and f(w; æ. v. denotes the per-sample loss.
The FL training proceeds in communication rounds and involves the following steps: (i) Initialization: The
server initializes the global model parameters w and broadcasts them to all clients. (ii) Client Sclection: In round f, the server selects a subset of clients K C KC, with |C*| = I*, based on a client selection strategy x. (iii) Local Update and Aggregation: Fach selected client i E AC performs local training and returns an updated model wt to the server. The server then aggregates these updates to form the new global model:
w '+1
Σ iEK'
nf
w/
E
jex n
Steps (ii) and (iii) are repeated until convergence of the global model.
Mavericks. The term Maverick was introduced by Huang et al. (0022) to refer to clients that exclusively hold data from one or more class labels; that is, no other client in the FL system possesses samples from those classes. Let Mmav M denote the set of class labels exclusively owned by Mavericks. For a given client i and class label C M, we define D; ((x,)) y) D, y = as the subset of data with label A client is Maverick if D, includes samples from
more classes in Mmavs i.e.,
D. = D
D..
(4)
cEMiaav
of od
A non-Maverick client only contains data from the remaining classes:
D. = U D..
(5)
oRMaaar
4

---

Published in Transactions on Machine Learning Research (11/2025)
In practice, multiple clients may jointly own rare classes; we refer to these shared Mavericks.
Shapley Value (SV) for Client Valuation in FL. SV (Shapley 1971 is given by
Ghorbani & Zou 2019 of client
(1) V(2)
(c(K
Σ OE
(6)
K
(1
where denotes the SV for client i, and represents subset of the client set K. The utility function V(-) take any form that quantifies the value of given subset; in this work, in much of the FL literature, we use validation accuracy to measure utility.
In cooperative game theory, the SV framework is utilized to calculate the contribution of each player in a coalition. Computing the exact SV in Equation 66) requires retraining the FL model for all possible subsets of clients, which is computationally prohibitive. To overcome this limitation, we adopt SV approximation methods commonly used in the literature. Crucially, our proposed Maverick-Shapley mechanism augments existing SV approximations, including MR (Song al. 2019), TMR (Wei et al.) 2020), and GTG (Liu et al. to enable Maverick-aware estimation of client contributions, which we discuss in the next section.
2022
4 Proposed Method: FedMS
In this section, we first introduce the Maverick- -hreriic-Shhpley contribution score, class-wise SV mechanism for accurately cstimating client contributions. The detailed stops of Maverick-Shapley are presented Algorithm We then integrate this valuation inlo a client selection mechanism, termed FedMS, which prioritizes the most valuable clients each round based on their Maverick-Shapley contribution scores (see Algorithm 2 Table in Appendix presents the main parameters and notations of our proposed method.
4.1 Maverick-Shaplet
This section describes the proposed Maverick-Shapley client contribution score. When training model for multi-class lasks, the difficulty of learning each class is different. Particularly in the presence of Mavericks, rare classes are harder to learn than the others. In order to differentiate between classes and accurately com- pute the contribution of each client (Mavericks and non-Mavericks alike), we propose class-wise SV-basod contribution score. In particular, we use the class wise accuracy as the utility function SV computations and aggregate class-wise contributions into single number using the difficulty level of each class. Class-wise accuracy is calculated as
Noe
Ve Vi (w;; Deat)
Σ Noj VcEM,
JEM
where w is a given model, Dvai is validation dataset at the server, N represents the number of validation data points of class e predicted as class j, M is set of class labels. Our main distinction in SV computation is the fact that we compute it in class-wise manner to better capture the diverse resources of Mavericks.
In each FL round, after receiving model updates from the participating clients, the server computes the SV of client i for class c, o5, by utilizing a gradient- SV approximation method of its choice using 7 It then computes the accunulated class-wise SVs Se using decay factor
S = S + (1 a1 a) dG VcE.
(
TTe class-wis accuracy equivalent per-class recall, measuring the proportion of correctly predicted samples within each class.
5

---

Published in Transactions on Machine Learning Research (11/2025)
Finally, the server computes the Maverick-Shapley contribution score of each client as weighted sum of Sţs in each training rounc
S = 3 ViEK
(9)
cEM
where 8 denotes the class difficulty of class in the current training round, adaptively adjusting the impact of each class in the contribution scores such that
1- Vann (w:Doal)
exp
5r
Ve M,
(10)
1- (1 clacy
(VVanee(u::Da)) (w::onl)
erp .EM
where the lemperature T controls the distribution. Since the difficulty of learning each class is dynamically changing, the server updates the class difficulty 8 and the contribution score $ in each round.
Our proposed Maverick-Shapley approach is universally applicable to the existing gradient-based SV approx- imations, as simply change the utility function and utilize class difficulties B. In this paper, we integrate Shverick-Shapley approach with three gradient-bart based SV approximations including MR (Song et al. 2019 TMR (Wei et al. 2020) and GTG (Liu et al. 2022). In the ensuing, we describe Maverick- Sharilly GTG. Details on Sexverick-Shaples MR and Seaverick-Shapler TMR are given in the Appendix.
Maverick-Shapley GTG (MS-GTG). MS-GTG is built on top of the Guided Truncation Gradient Shap- ley (GTG) approach (Liu al. 2022 by modifying the utility function and incorporating class difficulties shown in Algorithm 1] The GTG approach designs the guided sampling (Line 11 in Algorithm [ to improve the efficiency of subset model reconstructions and removes unnecessary reconstructions using trun- cation techniques (Line and 15 in Algorithm D. We utilize the following convergence criterion (Liu et al. 2022 for MS-GTG
[--j]
10
< 0.05,
(11)
KC X
10
where r is the number of iterations inside MS-GTG (Line Algorithm class-wise SV, of"
o rl weighted sum of its
Σ g of, and represents the number of preceding terms considered in the sum. C
After guided sampling, the server computes the dass-wise SV (line 22 in Algorithm D identifies the coreset F and updates the class difficulty & (lines 24 and 26 in Algorithm 1 The coreset is defined as the subset of sampled clients K' whose aggregation maximizes total class-wise validation accuracy. The algorithm thus outputs the per-client SV Φ, the class difficulty B, and the coreset K
4.2 Maverick-Shaplee Client Selection
The Maverick-Shappey contribution score we propose is a principled way for assessing the value of clients with rare data in FL. In this section, we describe how these contribution scores can be utilized by the server to make client selection each round. In a nutshell, based on these contribution scores, the server seleets the most contributing clients in each FL training round. This process is described in Algorithm 2 In each round the server calculates the selection probability of clients according to their contribution scores $ as
eap(S.) Σ erp(S.)) SEK
P..
ViEK
(12)
and samples clients based on P3 in each round. Since Mavericks exclusively owns certain classes, they are the primary contributors for these rare classes and are more likely to be selected when the model underperforms on them.
2In the first round, the server queries model updates from clients and initializes contribution scores based on the class wise accuracy these updates.

---

Published in Transactions on Machine Learning Research (11/2025)
Algorithm 1: Maverick-Shapley GTG (MS-GTG)
Input: Updated client models (ere current server model w'; validation dataset at server Drat; class- wise accuracy function Vetass();; M: set of class labels; X²: set of clients in round t; Hyperparameters: Error threshold ca, 6,, temperature T.
Initialize: Φi = 0, Vi E K =0
Compute w XodelAveraga(ua, (w]] }agKt)
Uo = Velune (w'; Dvai), un = Vetnaa (wo") Deat), // between round fruncation if luy nal > 6 then
++1
while Convergence criteria not met do for client E K do
10
++1 f Random permutation KC' \ {i) with -111 =
11
12
13
// mithhin-round truncation
14
for j L..... do
15
if JuN |r]
112
H [:
17
wH (ou
18
[r]
Velars (w:; D
19
else
(r) e
for class M do
6 &M1 = - + ( tr 19
22
23 Find coreset R³ and its clssswise accuracy 0
24
k 0 argmax, Σ
(WH: Deni)
cEM
25 // Obtain class difficulty 8
26 8 eap(" epp!
T
E M
6EM
return Φ, K3
Although clients are sampled from P3.s including all sampled clients K in aggregation is not always optimal. The collective contribution of group depends on how their updates interact: contribute redundant gradients even destabilize the aggregation, while others (e.g., Mavericks with rare classes) provide complementary information that significantly improves generalization. To ensure aggregation emphasizes the most beneficial interactions, the server identifies the coreset R through Saverick-Shaple. and aggregates only their updates.
sampled clients may
Shapley Rewards (SR). In each round, the server computes the class-wise SV of a selected client for each class c, ds. It then calculates the SR of each client i for round weighted of its class-wis wise SVs using the current class difficulty 8
R = Br.
80.0 0i Wek. cEM
(13)
5 Evaluation
We comprehensively evaluate the effectiveness of our algorithm, FodMS, on two datascts against six basclines, in terms of (I) model performance, (II) contribution allocation across Mavericks and non- Non-Mavericks, and (III) robustness against ad adversaiees and free-riders.

---

Published in Transactions on Machine Learning Research (11/2025)
Algorithm 2: FedMS: a Maverick-Shapley Client Selection Mechanism for FI.
Input: T: number of training rounds; E: number of local epochs; K": set of clients in round t; Dj: dataset of
client 1; B: minibatch size; ni: dataset size of the ith client round f; M: set of class labels; Deet validation dataset; Velaze (-): class-wise accuracy function; di learning rate at client i.
Server executes:
Initialize wo, S
for each round L = 0, T do
// Sample clients from Ps,.
exp(&,) Vi K Ps.: = Σ exp18;)
AC' sample clients Ps.
for each client i KC' in parallel do
w + UserUpddae (w',i)
// Calculate wlsss-wise SV di, class difficulty 8 and coreset K
10
6, B, Rr + Soveeric-Shaplien (fw") w', Dont, Velum()), M)
12
1/ Compute the accumulated class-wis wise SV S..
5 S (1 a)- &i, Vi.Vic K", Ve E M
1/ Compute contribution score St.
S. Σ 8 Sr E
15
16
w
Σ EER
w6;
17
function UserUpdate (w', 1):
for each local epoch E...' do
D. +- select a minibatch of size B C D, 20 wi f- w VD, w!)
19
21
return wt to server
5.1
Evaluation Setup
Datasets. We use five benchmark datascts, (1) MNIST Deng, 2012 consisting of handwritten digits, with
60,000 samples for training and 10,000 for testing, and (2) CIFAR-10 (Krizhevsky et al. 2009) consisting of colored images of 10 classes, with 50,000 samples for training and 10,000 for testing. Another 3 datasets are from MedMNIST (Yang et al., 2021 2023), large-scale MNIST-like collection of standardized biomedical images. (3) BloodMNIST (Acevedo et al. 2020) including blood cell microscope data, with 11,959 samples for training, 1,712 samples for validation and 3,421 for lesting.
(4) OrganAMNIST (Bilic et al.) 2023 Xu] let al. 2019) including abdominal CT data, with 34,561 samples for training, 6,491 samples for validation and 17,778 samples for testing. (5) PathMNIST (Kather et al.) 2019) consisting of colon pathology data, with 89,996 samples for training, 10,004 samples for validation and 7,180 samples for testing.
BloodMNIST, OrganAMNIST, and PathMNIST datasets are derived from clinical data such as blood cell images, abdominal CT scans, and colon pathology slides, and are widely used in the medical Al literature. They naturally exhibit class imbalance since some disease categories occur much less frequently than others. This property makes them well suited for evaluating our Maverick-aware framework because they reflect realistic rare disease scenarios where Maverick clients who hold data from rare or underrepresented classes are especially important.
Model. We utilize lightweight MLP neural network (Popescu et al. 2009) for MNIST and commonly employed CNN (Albawi et al. 2017 for the CIFAR-10, BloodMNIST, OrganAMNIST,, and PathMNIST dataset.
Validation datasct. For both MNIST and CIFAR-10 datasets, we follow common practice existing Shapley-based FL methods (MR, TMR, and GTG) by using balanced server-side validation set. For both MNIST and CIFAR-10, we randomly split 20% of testing samples validation dataset. In the BloodM-
8

---

Published in Transactions on Machine Learning Research (11/2025)
NIST, OrganAMNNIST, and PathMNIST datasets, we utilize the provided validation set which follows the imbalanced distribution of the training samples.
Maverick Scenarios. Both MNIST and CIFAR-10 datasets are uniformly distributed across all 10 class labels. BloodMNIST, OrganAMMNIST, and PathMNIST datasets are having imbalanced data distribution. These three datasets have different number of class labels, 8 class labels in BloodMNIST, 11 in OrganAM- NIST, and 9 in PathMNIST. In order simulate heterogeneous settings, we adopt the widely used practical data heterogeneity setting (Zhang et a 2023), where the heterogencity is controlled by the Dirichlet dis- tribution, denoted as Dir(y). Increasing y decreases the degree of heterogeneity. We vary Y to control the level of heterogencity and create three Maverick scenarios as follows: (i) Mavericks + Dir(10): Most IID setting (the data distribution among non- Maverick clients is identical.) (ii) Mavericks + Dir(1); Moderately heterogeneous setting, and (iii) Mavericks + Dir(0.1): Most Non-IID setting, where data distribution among clients is highly diverse.
In all three set senging,, we have 50 clients with 10% selection rale of all clients in each FL round. For MNIST and CIFAR-10 datasets, have 48 non- -oveericsss and 2 Mavericks, and each Maverick exclusively owns one rare class shown in Figure D (We randomly select class 5 and class 8 as Mavericks classes). For BloodMNIST, OrganAMNIST and PathMNIST datascts, we have 42 non-Mavericks and 8 Mavericks, cach rare class is shared by 4 Mavericks as shown in Figures 7 and For these imbalanced datasets, we select the classes with the fewest samples as the Maverick classes, since these correspond to clinically important but underrepresented cases in practice. We then distribute these rare classes to a small number of Maverick clients, mimicking hospitals or labs that hold limited but critical data. For all settings, Mavericks not only have data samples from rare classes but also from the other classes. Figures 1 7 and g also illustrate the three heterogeneity scenarios, with y set to 10, 1, and 0.1, respectively. Additionally, we consider 5 clients setting (4 non-Mavericks and 1 Maverick) without client selection (i.e., all 5 clients participate in each round). In 5 clients setting, the data distribution of -averriisssss is IID and the Maverick exclusively owns one class.
g
Adversarial and Free-riding Scenarios. In this setting, we have 58 clients (48 non-Mavericks, 2 Mav- ericks, 6 adversarial clients and 2 free-riders). We consider the existence of adversarial clients during the federated training process. They can mislcad the model by injecting poisonous data or model updates. In our experiments, we include three types of malicious behaviors. (i) Label-flipping clients who flip the label of their dataset (Shen et al.) 2023). (ii) Pata--eisoned clients who participanting training with random generated data (Sandeepa et al. 2024]. .. (iii) Update-poisoned clients who send random generated model updates to the server during the FL training (Xie et al. 2024). We also examine the existence of free riders (Chen et al.] 2024]. Free-riders do not update the model, but instead send the same model back to the server. Free-riders can enjoy the trained global model without any effort, whereas the benefits of benign users are greatly compromised.
Baselines. We consider seven client selection baselines: FedAvg MMMahan et al. 2017). S-FedAvg Nagala- patti & Narayanam 2021), FedEMD (Huang et al. 2022). FodProx (Li et al., 2020a). GreedyFed (Singaal et al. 2004), PoC (Cho el al. 2022), and HiCS-FL CCen & Vikalo, 2024). FedAvg applies random sampling in each round. S- S-FedAvg and GreedyFed combine SV-based methods with client selection. S-FedAvg adopts MR (Song et al. 2019) and GreedyFod uses GTG (Liu et al. (2022) in their client selcction. FedProx, PoC, and HICS-FL propose mechanisms regarding data heterogeneity in FL. FedEMD combines EMD distance with client selection in the presence of Mavericks.
FL Sctup. We train 100 rounds for MNIST and 200 for CIFAR-10, both with batch size of 64. We employ 5 local training rounds for MNIST and single local training round for CIFAR-10. We train BloodMNIST for 200 rounds and ArgNST for 100 rounds. PathMNIST is trained for 100 rounds under the Mavericks + Di0 and Mavericks + Dir(1) settings, and 200 rounds under the Mavericks + Dir(0.1) setting. Under the BloodMNIST, OrganAMMISTT, and PathMNIST datasets, we have one local training round and use a batch size of 64. The learning rate is 0.05 for all baselines in all datasets. Our algorithm is implemented in Pytorch and we perform experiments on two NVIDIA RTX A5000 and two Xcon Silver 4316.
Evaluation Metrics. To assess the effectiveness of evaluated mechanisms, we consider the test accuracy, Macro-F1 score (averaged across all classes), and recall as the utility metrics. For the datasets with balanced
9

---

Published in Transactions on Machine Learning Research (11/2025)
Table 1: Test accuracy (%) different algorithms across three Mavericks scenarios on MNIST and CIFAR-10 dataset, with 50 clients under client selection.
Settings
Mavericks Dir(10)
Mavericks +
Dir(1)
Mavericks Dir(0.1)
Methods
MNIST
Cifarl0
MNIST
Cifar 10
MNIST
Cifar10
FedArg
79.13+0.29
63.36#0.29
79.00=#0.14
61.52=0.91
74.55=2.48 48
48.68=0.88
FodProx
78.53.1117
59.84110.93
77.54.10.30
37.0712.04
75. 10.332
41.06166.8
PoC
91.27++0.71
67.07+0.98
78.76+00.23
60.18+4.26
54.63+2.62
54.77=6.38
FedEMD
+0.51+
55.84+0.40
78.05++0.96
55.66+0.58
65.39++2.93
47.18-0.90
S-FedAvg
80.6642..00 30
62.98:10229
52.56:11.88
61.42-0.44
77.24:1088
45.94
GreedyFed
80.97++3.55
60.60++044
82.47++3.16
58. 08.009
89.73+1.40
51.97+3.92
FedMS
92.96=0.32
65.92+00.8
92.67=0.10
65.92=00775
89.62=0.57
59.75+22110
Table 2: Macro-F1 scores of different algorithms under the Mavericks + Dir(10) setting across three datasets
(BloodMNIST, OrganAMNIST, and PathMNIST) with 50 clients under client selection. Values in paren- theses indicate recall on rare classes.
Methods
OrganAMNIST
PathMNIST
BloodMNIST
FedAVG
0.65 0.01 (0.18 0.02)
0.51 0.03 (0.01 0.02)
0.47 0.12 0.12 (0.21 = 0.26)
FodProx
0.01 (0.07 1 0.05)
(0.00 1 0.00)
0.48 0.08 (0.21 = 0.23)
POC
0.66 0.02 (0.19 1 0.09)
(0.01 + 0.01)
0.50 0.04 (0.13 = 0.22)
FedEMD
0.67 0.01 (0.25 1 0.09)
(0.07 1 0.06)
0.51 1 0.03 (0.00 + 0.00)
GroedyFed
0.65 0.08 1
0.50
(0.00
0.50 0.01 (0.00 = 0.00)
S-FedAVG
0.68 + 0.01 (0.29 = 0.06)
0.50 (0.02 - 0.04)
0.51 $6100.000 0.09 (0.04 = 0.07)
HiCS-FL
0.66 + 0.02 (0.38 + 0.05)
0.59 1 0.01 (0.25 0.11)
0.43 1 0.06 (0.17 = 0.29)
FedMS
0.66 0.04 (0.69+0.03)
0.66 + 0.06 (0.68+0.26)
0.50 + 0.01 00.35-00.09
distribution like MNIST and CIFAR-10, we use lest. accuracy as the main metric, as shown in Table For the datasets with imbalanced distribution including BloodMNIST, OrganAMNIST, and PathMNIST, we report recall of the Maverick classes and Macro-Fl score as our main evaluation metrics, as shown in Tables 2 and In addition, we evaluate different schemes basod on their Shapley Rewards (SR) to the Mavericks. A larger SR is associated with higher contributions. We also utilize the participation rate (%) of Mavericks, adversaries, and free-riders the robustness metric, as shown in Table 5
Hyperparameter Settings. We tune the hyperparameters of all baselines using grid search. In FedProx, we set the weight of proximal term a as 1 (selecting from [0.1, 1]). For PoC, we adopt the adaptive variant aadapow-d and set the decay factor as 0.9. In FedEMD, we set the distance coefficient (denoted by 3 in that scheme Huang et al.] (2022)) as 0.01. We choose a = 0.7 for S-FedAvg and GreedyFed. In S-FedAvg, we utilize MR. to calculate SV of all permutations (without using the Monte-Carlo sampling). For HiCS-FL, the scaling parameter T (temperature) used in data heterogeneity estimation was set to 0.0025. For our proposed FedMS, we choose temperature T = 0.01, a 0.8, and error threshold é; 0.01, 6, = 0.001. In each round, we calculate the class-wiss wise accuracy difference and sum it to obtain d', which measures the overall change in class-wise performance between the coreset K* and the initial global model of round t. If the model performance of coresct K diverges significantly from the initial global model of round t (i.c., d' < 0 and |d' > ea), the server discards the coreset and does not update the model for round t. We initially set ca to 3.0, and it exponentially docays to 0.1 by the end of training in all scttings.
5.2 Evaluation Results
Main Result I: FedMS Improves Model Performance. Figure 2 iillstrates how the test accuracy and SR change during training in the 5 client setting (without client selection). We evaluate the test accuracy under two settings: Accuracy With All Chienss and Accuracy Without Mavericks. In Figure 2 there is a gap between the Accuracy With All Clients setting and the Accuracy Without Mavericks setting, and this gap reveals the importance of Mavericks. That is, without leveraging the Mavericks, the full potential of the model cannot be achieved, motivating our FedMS client selection mechanism.
10

---

Published in Transactions on Machine Learning Research (11/2025)
Table 3: Macro-Fl scores of different algorithms under the Mavericks + Dir(1) setting across three datasets (BloodMNIST, OrganAMNIST, and PathMNIST) with 50 clients under client selection. Values in paren- theses indicate recall on rare classes.
Methods
OrganAMNIST
PathMNIST
BloodMNIST
FedAVG
0.22:0005 (0.17 0.06)
0.47 0.06 (0.00 + 0.00)
0.55
0.03 (0.21 + 0.22)
FedProx
0.54 (0.09 0.08)
0.38 0.13 (0.00 = 0.00)
0.51 = 0.03 (0.05 + 0.07)
POC
0.63 (0.15+0.11)
0.52+0.01 (0.02 0.02)
0.42 0.10
(0.12 0.17)
FedEMD
0.66+0001 (0.30+0.10)
0.51 0.04 (0.04 0.06)
0.54 0.04
(0.12+0.14)
Fre
+..1+ (0.00+0.00)
0.51 0.03 (0.00 100+0000 0.00)
0.52 0.00 (0.00 0.00)
AVG
0.64 0.04 (0.19+0..7))
0.50 + 0.03 (0.06 + 0.09)
0.50 0.06 (0.34+0.33)
IRCS-FL
0.66 + 0.03 (0.38 0.12)
0.57 = 0.04 (0.30 0.20)
0.46 = 0.03 (0.00 0.00)
FedMS
0.67 + 0.02 (0.5411.07)
0.64 0.03 (0.72.10.14)
0.49 + 0.02 (0.41110.41)
Table 4: Macro-F1 scores of different algorithms under the Mavericks + Dir(0.1) setting across three datasets (BloodMNIST, OrganAMNIST, and PathMNIST) with 50 clients under client selection. Values in paren- theses indicate recall on rare classes.
Methods
OrganAMNIST
PathMNIST
BloodMNIST
FodAVG
0.50 1 0.06 (0.27 1 0.02)
0.09 (0.07 0.08)
0.16 (0.17 0.29)
FedProx
0.44 1.005 (0.27 1 0.02)
0.11 (0.09 0.15)
0.00 (0.16 0.29
POC
0.42 0.14 (0.16 0.15)
(0.04 0.08
0.05)
0.03 (0.04 0.04)
FedEMD
0.50 4: 0.09 (0.22 + 0:20)
(0.13 0.08
0.133)
1.40 (0.09 = 0.16)
GreedyFed
0.55 4 0.03 (0.33 : 0.12)
0.05 (0.00 1 0.00)
0.37 0.09 (0.00 = 0.00)
AVG
0.52 + 0.00 (0.27 0.04)
0.00 (0.14+0.077
0.48 + 0.03 (0.22 + 0.22)
HICS-FL
0.45 + 0.07 (0.10 0.08)
0.43 (0.07 0.07)
0.31 0.09 (0.21 0.36)
FedMS
0.62 + 0.02 (0.39 + 0.25)
0.60 + 0.12 (0.48=0.42)
0.48 + 0.11 (0.55=0.31)
Comparing the accuracy of the proposed FedMS vs. the baselines for the MNIST and CIFAR-10 datasets under all three Maverick scenarios are given in Table ! In all three scenarios from IID to Non-IID settings, our proposed FedMS maintains high accuracy and achieves better model performance than baselines in most settings. Among the bascline methods, only S-FedAvg and GreedyFed adopt SV in their client selection process but none of them considers the Mavericks settings. In those SV-based methods, the low SR of Mav- ericks decreases their selection probability during FL training, resulting in under-utilization of the Mavericks and poor performance of global model. Since FedMS effectively identifics the most contributing clients in a class-wise manner, successfully selects the Mavericks and improves model performance. As shown in Tables 3 and 4. results on the BloodMNIST, OrganAMNIS" and PathMNIST datasets further demonstrate that our FedMS consistently improves Maverick-class accuracy, ensuring that rare or underrepresented classes are better learned and not overlooked, while maintaining strong overall model performance.
FedEMD) applies decreasing selection probability for the Mavericks as training rounds progress. As indicated in Figure 2 the accuracy gap between the Accuracy With All Clients setting and the Accuracy Without Mavericks setting increases as the training progresses to later rounds, demonstrating the need for selecting the Mavericks in later training rounds as well. Our approach differs from FedEMD by not relying on the distance of local and global data distributions in making client selection decisions. Instead, we prioritize the class-wise contribution of each client. during the selection process, thus, our method achieves robust SR and improved accuracy compared to FedEMD. Additionally, querying local data distributions in FedEMD may lead to privacy leakage for clients, whereas our method does not require extra information from clients.
Main Result II: FedMS Aligns Shapley Rewards (SR) with Actual Client Contributions. FedMS computes SR that better reflect client contributions by leveraging class-wise SV and class difficulties B. If rare classes owned by the Mavericks perform poorly on the validation dataset, our mechanism incrcases the class difficulty associated with these rare classes. Hence, our system provides SR that accurately capture the contributions of Mavericks.
11

---

Published in Transactions on Machine Learning Research (11/2025)
Test Accuracy
0.7
0.4
Shapley Rewards
Test Accuracy
0.7
0.8-
0.4
Shapley Rewards
Accuracy With Ciinnss
Accuracy With All Cients
0.8
Without
0.8
Without Maveridks
Aug Shapley of
0.2
Shopley Non Maverioks
0.2
0.5
Aug Shopley of Movericss
0.5-
Aug Shapley Mavericks
0.4
0.0
0.0
-0.1
0.1
20
80
100
60
100
Training Rounds
Training Rounds
(a) GTG
(b) MS-GTG
Figure 2: Comparison of test accuracy and Shapley rewards with clients (without client selection) for the MNIST dataset using GTG and MS-G GTG under FedAvg.
Accuracty
45
Accuracy
63
Accuracy
45
Aug Shapley of Non Mavericks
Avg Shapley of Non Mavericks
Aug Shapley of Non Mavericks
Text Accuracy
fvg Shapley
Shapler Shapley Rewards
0.6
Jvg Shapley
0.3
Shapley eaapley Rewards
Text Accuracy
2.6
Avg Shapley
0.3
Shap lyy Rewards
12
6.2
25
0.1
0.4
Test
0.0
-03
-01
15 Training
107
129 Training Rounds
75
105
Rounds
Training Rounds
(a) FedMS (Our method)
(b) S-FedAvg
(c) GreedyFed
Figure 3: Comparison of test accuracy and Shapley rewards with 50 clients (with client selection) for the CIFAR-10 dataset under the Mavericks + Dir(10) (most IID) setting using MS-GTG for FedMS, MR for S-FedAvg and GTG for GreedyFed.
In Figure 2 we see that global model accuracy increases with the participation of the Mavericks. Despite their critical importance for improved accuracy, in Figure 4 when considering the scenario with 50 clients (with client selection), we observe that the average SR. of the Mavericks is considerably lower than those of the non-Mavericks in S-FedAvg and GreedyFed when the SR are obtained directly from MR and GTG, respectively In contrast, Figure 4a exhibits robust SR for Mavericks, reflecting their importance in later rounds for improved accuracy. In Figure the elevated SR for Mavericks show the superiority of MS-GTG over the standard GTG when Mavericks are involved in training. Even when there is no client selection, the main benefit of Hlaorick-Shapley is assigning SR that better reflect the contributions of Mavericks, demonst.raed in Figure b))
4a
When examining the SR, observe from Figure that FedMS allocates more rewards to Mavericks compared that
to non-Mavericks in the IID setting, in line with the observed accuracy benefit of training with the Mavericks. In contrast, S-FedAvg and GreedyFed provide lower rewards to Mavericks compared to non- non- Mon-Mavericks, Mavericks are easily identified as outliers by Maverick-unawaro Shapley methods like MR and GTG in the Mavericks + Dir(10) (most IID) setting.
In the Mavericks + Dir(0.1) (most Non-IID) setting, Figure Ic presents that the clients' data distributions are heterogeneous, with some non-Mavericks also possessing large number of samples from specific classes. As shown in Figure 5 S-FedAvg and GreedyFed Fed allocate lower rewards to non-Mavericks compared to Mavericks, even though the classes held by non- Non-MMaerickss are also important. In contrast, FedMS ensures a distribution of SR among Mavericks and non-Mavericks, as illustrated in Figure Our proposed FedMS assigns equal importance to all classes and distribute the SR proportionally based on each client's class contribution. Regarding the utility metric, we notice that FedMS better utilizes
more contribution-aligan
5a
IIn our evaluation, stick original implementations of the baselines GTG Liu al. 2022 caleulate SR GreedyFed, and MR (Song et 2019) for S-FeAAvg.
12

---

Published in Transactions on Machine Learning Research (11/2025)
Accursity
-55
Accuracy
6.
Accuracy
43
Aug Shapley of Non Mavericks
Avg Shapley of Non Mavericks
Aug Shapley of Non Mavericks
Accuracy
fug Shapley Mavericks
Shapley Rewards
0
Avg Shapley
Shapley Rewards
Test Accuracy
2.
Avg Shapley
Shapley Rewards
12
6.2
6.
-0.
-0.
-01
107
73 101
123
Training Rounds
Training Rounds
Training Rounds
(a) FedMS (Our method)
(b) S-FedAvg
(c) GreedyFed
Figure 4: Comparison of test accuracy and Shapley rewards with 50 clients (with client selection) for the CIFAR-10 dataset under the Slvericks-Dir() setting using MS-GTG for FodMS, MR for S-FedAvg and GTG for GreedyFed.
Accuracy
45
Accuracy
665
Accuracy
65
Aug Shapley of Non-Mavericks Aug Shapley Mavericks
Avg Shapley of Non-Mavericks
Avg Shapley of Kon-Mawerikks
Shapley Rewards
Avg Shapley Mavericks
Shapley Rewards
Avg Shapley of
Shapley Rewards
Test Accuracy
1.0 1.5 1..
0.6
Test Accuracy
2.5
1.2
6.
9.5
42
15 120
103
125
75 10s
125
Training Rounds
Training Rounds
Training Rounds
(a) FedMS (Our method)
(b) S-FedAvg
(c) CreedyFed
Figure 5: Comparison of test accuracy and Shapley rewards with 50 clients (with client selection) for the CIFAR-10 dataset under the Mavericks + Dir(0.1) (most Non-IID) setting using MS-GTG for FedMS, MR for S-FedAvg and GTG for GreedyFed.
Mavericks, resulting in an overall improvement in model accuracy compared to S-FedAvg and GreedyFed on both Mavericks + Dir(10) setting and Mavericks + Dir(0.1) settings, as also demonstrated in Table
Main Result III: FedMS Shows Robustness to Adversaries and Free-riders. Table 5 shows the participation rate (%) of Mavericks, adversaries and free-riders in FL baseline methods. A low participation inject harmful updates and freo-riders
rate of adversarics and free-riders is critical, since adversarics contribute no useful updates, both of which undermine global training. Conversely, high participation rate of Mavericks is desirable, as their data captures rare or underrepresented classes and directly improves gener- alization on these classes. As shown in Table 5 FedMS achieves this dual objective: it effectively suppresses adversarics and frec-riders while maintaining strong inclusion of Mavericks. contrast, bascline methods show a trade-off: some reduce adversarial and free-riders participation but fail to select. Mavericks, while others increase Mavericks participation but allow adversaries and free-riders into training. This demonstrates that FedMS balances robustness to adversaries and free-riders with the inclusion of Mavericks.
6 Discussion
Rewards Incentives for Participation. Throughout this paper, we utilized Mavericks in FL, to improve both the model performance and the rewards given to clients. Model performance is important from the server's perspective, which can be, 6.9., companies, hospitals, or institutions. Properly assessing the contribution and rewarding participants to participate in the FL training process, is equally important. Clients, like the Mavericks who own rare data, are valuable the server and should receive rewards that reflect their contributions. Without proper incentives or rewards, Mavericks may refuse to join the FL training if their contributions severely undervalued or, even worse, if they deemed outliers.
13

---

Published in Transactions on Machine Learning Research (11/2025)
Table 5: The participation rate (%) of Mavericks, adversaries and free-riders in FL methods under the MNIST dataset with Mavericks Dir(0.1) setting.
Settings
Label flipping
Data poisoneds
Update-poisoned
Free-riding
Mavericks
FedAvg
9.5%
6.0%
7.0%
12.0%
9.0%
FedProx
9.0%
10.5%
8.5%
9.5%
6.3%
PoC
8.0%
9.5%
8.0%
9.0%
8.0%
FedEMD
0.0%
1.0%
0.0%
0.5%
1.5%
S-FodAwg
7.0%
10.0%
8.5%
8.5%
10.0%
GreedyFed
1.0%
60.0%
1.0%
42.5%
47.0%
FedMS
0.5%
0.0%
1.0%
2.5%
54.0%
In this work, we aim to assign Shapley rewards and use class difficulty B represent. the importance of each class each training round. Our approach encourages clients with valuable data to participate the FL training. By recognizing the contributions of the Mavericks, we establish a contribution-guul rewarding mechanism for all participants in the FL system, each potential client knows that their unique contributions would be properly acknowledged.
7 Conclusion
Client selection plays a pivotal role in FL, especially in the presence of Mavericks, as allows for the optimization of the utility derived from diverse model updates. In this paper, we propose FedMS, Maverick- aware Shapley valuation mechanism for client selection in FL that not only provides accurate evaluation of Mavericks contributions but also effectively selects the most contributing clients in each FL round. We show that our FedMS achieves better model performance and delivers a contributionn-aiggeed distribution of Shapley rewards compared to state-of-the-art approaches, while providing robustness against adversaries and free-riders.
Acknowledgments
This work was partially supported by the National Science Foundation (NSF) under Awards 1956393 and 1900654 at UC Irvine, and Award 1956435 at USC.
References
Andrea Acevedo, Anna Merino, Santiago Alférez, Angel Molina, Laura Boldú, and José Rodellar. dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data in brief, 30:105474, 2020.
Saad Albawi, Tareq Abed Mohammed, and Saad Al-Zawi. Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET), pp. 1 6. leee, 2017.
Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, et al. The liver tumor seg- mentation benchmark (lits). Medical image analysis, 84:102680, 2023.
Baturalp Buyukates, Chaoyang He, Shanshan Han, Zhiyong Fang, Yupeng Zhang, Jicyi Long, Ali Fara- ControCCooorbbbiinnBeeeee Design for Collaborative Machine Learning
hanchi, and Salman Avestimehr. on Blockchain. In JEEE International Conference on Decentralized Applications and Infrastructures (IEEE DAPPS 2023), July 2023.
Bingyang Chen, Tao Chen, Xingjic Zeng, Weishan Zhang, Qinghua Lu, Zhaoxiang Hou, Jichan Zhou, and Sumi Helal. Diml: Dynamic foderated meta-learning for rare discase prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21 (4)8888 889, 2023.
14

---

Published in Transactions on Machine Learning Research (11/2025)
Huancheng Chen and Haris Vikalo. Heterogeneity--uudee client sampling: Towards fast and efficient non-iid federated learning. Advances in Neural Information. Processing Systems, (1155525-65500 2024.
Mengmeng Chen, Xiaohu Wu, Xiaoli Tang, Tiantian He, Yew Soon Ong, Qiqi Liu, Qicheng Lao, and Han Yu. Free-rider and conflict aware collaboration formation for cross-silo federated learning. Advances in Newral Information Processing Systems, 37:54974-55004, 2024.
Yae Jee Cho, Jianyu Wang, and Gauri Joshi. Towards understanding biased client selection in federated learning. In International Conference on Artificial Intelligence and Statistics, pp. 10351-10375. PMLR, 2022.
Li Deng- The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 2966)1141-142, 2012.
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach. Advances in Neurol Information Processing Sys- tems, 33:3557-3568, 2020.
Lei Fu, Huanle Zhang, Ge Gao, Mi Zhang, and Xin Liu. Client selection in federated learning: Principles, challenges, and opportumities. IEEE Internet of Things Journal, 2023.
Amirata Ghorbani and James Zou. Data shapley: Equitable valuation of data for machine learning. In International Conference on Machine Learning, pp. 2242-2255 PMTR, 2019.
Avishek Ghosh, Jichan Chung, Dong Yin, and Kannan Ramchandran. An efficient framework for clustered federated learning. Advances in Neural Information Processing Systems, 33:19586-19597, 2020.
Jiyue Huang, Chi Hong, Yang Liu, Lydia Y Chen, and Stefanie Roos. Tackling Mavericks in Federated Learning via Adaptive Client Selection Strategy. In Proceedings of the AAAI Conference on Artificial Intelligence, 2022.
Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, and Phillip B Gibbons. Federated learning under distributed concept drift. In International Conference on Artificial Intelligence and Statistics, pp. 5834-5853. PMLR, 2023.
Yvonne Kamegne, Eric Owusu, and Helina Oladapo. Alexa, why can't you hear our accents: Cross cultural studies on the inclusivity of voice recognition systems. In International Conference on Humon- Computer Interaction, pp. 22-71. Springer, 2025.
Jakob Nikolas Kather, Johannes Krisam, Pornpimol Charoentong, Tom Luedde, Esther Herpel, Cleo-Aron Weis, Timo Gaiser, Alexander Marx, Nektarios A Valous, Dyke Ferber, et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS medicine. 16(1)=e1022330, 2019.
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. Cifar-10 (canadian institute for advanced rescarch). Technical Report 1, Technical Report, 2009.
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimization in heterogencous networks. In Proceedings of Machine Learning and Systems, volume 2, pp. 429 450, 2020a.
Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, and Qi Dou. Fedbn: Federated learning on non-iid features via local batch normalization. In International Conference on Learning Representations, 2020b.
Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu, and Lizhen Cui. GTG-Shapley: Efficient and accurate participant contribution evaluation in federated learning. ACM Transactions on Intelligent Systems and Technology (TTST), 13(4):1-21, 2022.
15

---

Published in Transactions on Machine Learning Research (11/2025)
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-effiinn learning of deep networks from decentralized data. In Artificial Intelligence and Statistics, pp. 1273 1282. PMLR, 2017.
Shira Michel, Sufi Kaur, Sarah Elizabeth Gillespie, Jeffrey Gleason, Christo Wilson, and Avijit Ghosh. "it's not representation of me": Examining accent bias and digital exclusion in synthetic ai voice services. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, pp. 228-222--20 2025.
Lokesh Nagalapatti and Ramasuri Narayanam. Game of Gradients: Mitigating irrelevant clients in federated lcarning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pp. 9046-9054, 2021.
Marius-Constantio Popescu, Valentina E Balas, Liliana Perescu-Popescu, and Nikos Mastorakis. Multilayer perceptron and neural networks. WSEAS Thansactions on Circuits and Systems, 8(7):579-588, 2009.
Chamara Sandeepa, Bartlomiej Siniarski, Shen Wang, and Madhusanka Liyanage. Sherpa: Explainable robust algorithms for privacy-preserved federated learning in future networks defend against data poi- soning attacks. In 2024 IEEE Symposium on Security and Privacy (SP), pp. 4772 4790. IEEE, 2024.
Stephanie Schoch, Haifeng Xu, and Yangfeng Ji. Cs-shapley: class-wise shapley values for data valuation in classification. Advances in Newral Information Processing Systems, 35:34574 34585, 2022.
Lloyd S Shapley. Cores of convex games. International Journal of Game Theory, 1:11 26, 1971.
Xicong Shen, Ying Liu, Fu Li, and Chunguang Li. Privacy-preserving foderated learning against label-flipping attacks on non-iid data. IEEE Internet of Things Journal, 11(1):1241-1255, 2023.
Pranava Singhal, Shashi Raj Pandey, and Petar Popovski. Greedy Shapley Client Selection for Communication-Effeee Edffcieen Federated Learning. IEEE Networking Letters, 2024.
Tianshu Song. Yongxin Tong, and Shuyue Wei. Profit allocation for federated learning. In 2019 IEEE International Conference on Big Data (Big Data), pp. 2577 2586. IEEE, 2019.
Xinru Song, Feng Wu, Zongchao Xie, Longxiang Gao, Lei Cui, Youyang Qu, and Shujun Gu. Grouped fed- erated meta-learning for privacy-preserving rare disease diagnosis. In 2024 International Joint Conference on Neural Networks (LJCNN), pp. 1-8. IEEE, 2024.
Nurbek Tastan, Samar Fares, Toluwani Aremu, Samuel Horvath, and Karthik Nandakumar. Redefining contributions: shapley-driven federated learning. In Proceedings of the Thirty-There Third International Joint Conference on Artificial Intelligence, pp. 5000-5017, 2024.
Shuyue Wei, Yongxin Tong, Zimu Zhou, and Tianshu Song. Efficient and fair data valuation for horizontal federated learning. Federated Learning: Privacy and Incentive, pp. 139-152, 2020.
Yueqi Xie, Minghong Fang, and Neil Zhenqiang Gong. Fedredefense: Defending against model poisoning attacks for federated learning using model update reconstruction error. In International Conference on Machine Learning, 2024.
Xuanang Xu, Fugen Zhou, Bo Liu, Dongshan Fu, and Xiangzhi Bai. Efficient multiple organ localization in ct image using 3d region proposal network. IEEE transactions on medical imaging, 3888)1188511889 2019.
Jiancheng Yang, Rui Shi, and Bingbing Ni. Medmnist classification decathlon: A lightweight automl bench- mark for medical image analysis. In IEEE 18th International Symposium on Biomedical Imaging (ISBT), pp. 191-195, 2021.
Jianeheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, and Bingbing Ni. Medmnist v2-a largo-scale lightweight benchmark for 2d and 3d biomedical image classification. Scientific Dala, 10(1 (11, 2023.
16

---

Published in Transactions on Machine Learning Research (11/2025)
Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, and Haibing Guan. Fedala: Adaptive local aggregation for personalized federated learning. In Proceedings of the AATI Conference Artificial Intelligence, volume 37, pp. 11237 11244, 2023.
A Appendix
This appendix extends the main body of our paper, providing supplementary materials to enhance the un- derstanding of our proposed method. This appendix first introduces additional Maverick-aware Shapley ap- proximations, namely MS-MR and MS-TMR. Then, we provide data samples distribution for BloodMNIST, OrganAMNIST,, and PathMNIST datasets applied in the paper. Table i presents the main parameters and nolations of our proposed method and Figure 6 illustrates the FL. framework in the presence of Mavericks.
H
Client
Client
Amount
All clssses Mamer
Server
Client
Client (Maverick)
Client
Figure 6: FL framework in the presence of Mavericks. In this example, client 2 is Maverick, only that client possesses samples from the green label.
A.1
Maverick-Aware SV Approximations
Algorithm 3: Maverick-Shapley MR (MS-MR)
Input: Updated client models fwf current server model w'; validation dataset at server Dvati class wise accuracy function V-iass()); M: set of class labels; A': set of clients in round t; Hyperparameter: Temperature
Initialize: 0,Vi K
for each subset Q C K do
unq = Moooeelverrage((ui w²) for client E KC' do for class . do
$
Σ oCxtits)
ve
Valana (moutn))
De Ve (wee
(K21-1)
// Find the coreset Re' and ils class-wise accuracy 0 10 Kt, à +- argmax
Σ (wa Dvat)
cEM
// Oblain class difficully $
8 capp (2)
Vc M
exp( 1-
18 return Φ. R²
Maverick-Shappey MR (MS-MR). MS-MR, built on top of the Muuti--ouud (MR) approach (Song ct al. 2019). In MR, the SV is calculated every FL round and the subscts of client models are reconstructed by using the corresponding gradient updates. In our MS-MR, the server first calculates the class-wise SV (Line 8 in Algorithm 3 for each client, and then computes the contribution score S, of each client i with
17

---

Published in Transactions on Machine Learning Research (11/2025)
Algorithm 4: Naverick-Shapley TMR (MS-TMR)
Input: Updated client models (we current server model w'; validation dataset at server Dvat; class-wise accuracy function V-lasa(-); M: set of class labels; X²: set of clients in round t; Hyperparameter: Error threshold cb, Temperature T
Initialize: Φi E
Ug (w Dvai); ux Vednso ;Dval,,
// between round trancation
if |ex -vul > 66 then
for each subset Q - K do wq =oodelAverage( (ouee w')
for client i E K do for class e do
10
Vo claze (i) (wo:
11
6 = R
Σ QCKTI(
(K
12 // Find the coreset R and its closs-wise accuracy 0
13 K², 00+ + argmax
Σ
(Wiq Dvat)
cEM
14 // Obtain class difficulty 8
15 8 exp( 1-p8
M
16 return ④, B,
updated class difficulty B (Line 12 in in Algorithm 3) and the class-wisc SVs d. In cach round, the server selects the subset of clients Kº that achieve the highest accuracy increase and aggregates only their updates as the updated global model of the next round.
Maverick-Shapley TMR (MS-TMR). MS-TMR is built on top of the Truncated Multi-Rounds Con- struction (TMR) approach (Wei et al. 2020 The TMR extends the MR by introducing a decay parameter À to eliminate unnecessary subset model reconstructions. In our MS-TMR, we introduce an error threshold Eb to eliminate the unnecessary reconstructions (Line in Algorithm
Table 6: Notation and key parameters used in the paper.
Notation
Description
Data sumple with fosture wetlr and label Set class labels, At (1.2.....)
2,.,....
Learning rate of client
Local dataset of cliest
D..d
Validation
at
Global mudel weights
Local model weights of climnt
k.....))
accurccy
d
Class ise Shapley value vector foe all clients
Class-riin Shapley value of dient
Clasa diffieslly
clusees
difficulty for class
Accumdated Shapley vaiue of clicat for cluss fortce accumdlted Shapley value Coatefbution score of clicut for cluss e Clisat selection probuliiity vorlor oour all elints selection probubility client Number of FL, training wounds Index of FL round, t= O, I...... spoche
Number
Mini batch sise
Global model weights round
Local model weiphts eliest
atlected clienta
selectiion strntogy
ornert with the highest chesarcrmyy in roundl
client
18

---

Published in Transactions on Machine Learning Research (11/2025)
A.2 Data Samples Distribution for BloodMNIST, OrganAMNIST and PathMNIST Datasets
Figure 7 shows the data distribution in Organ dalaset across three Mavericks scenarios, with class 1 and class 2 as Mavericks classes. Figure g presents the data distribution in PathMNIST dataset across three Mavericks scenarios, with class and class as Mavericks classes. Figure shows the data distribution in BloodMNIST dataset across three Mavericks scenarios, with class 0 and class as Mavericks classes.
(a) Mavericks + (10
(b) Mavericks + Dir(1)
(c) Mavericks + Dir(0.1)
Figure 7: Data sample distribution across clients in three Mavericks scenarios for OrganAMNIST dataset. Mavericks are shown as blue circles, and non-Mavericks as red. Cirele size indicates sample count, with heterogeneity controlled by 7 in Dir(y). Higher 7 means lower heterogeneily. (a) is the most, IID setting with identical distributions for all but Mavericks. (b) shows moderate heterogeneity, and (c) is the most Non-IID. In all cases, the Mavericks exclusively hold certain class labels (e.g., class 1 and class 2), while still containing samples from other classes.
Chol
(a) Mavericks + (10
(b) Mavericks + Dir(1)
(c) Mavericks + Dir(0.1)
Figure 8: Data sample distribution across clients in three Mavericks scenarios for PathMNIST dataset. Mavericks are shown as blue circles, and non-Maverieks as red. Circle size indicates sample count, with heterogeneity controlled by in Dir(n). Higher means lower heterogeneity. (a) is the most IID setting with identical distributions for all but Mavericks. (b) shows modorate hetcrogeneity, and (c) is the most Non-IID. In all cases, the Mavericks exclusively hold certain class labels (e.g., class and class 6), while still containing samples from other classes.
(a) Mavericks + Dir(10)
(b) Mavericks + Dir(1)
(c) Mavericks + Dir(0.1)
Figure 9: Data sample distribution across clients in three Mavericks scenarios for BloodMNIST dataset. Mavericks are shown as blue circles, and non-Mavericks as red. Circle size indicates sample count, with heterogeneity controlled by y in Dir(y). Higher y means lower heterogeneity. (a) is the most IID setting with identical distributions for all but Mavericks. (b) shows moderate heterogeneity, and (c) is the most Non- In all cases, the Mavericks exclusively hold certain class labels (e.g., class O and class 4), while still containing samples from other classes.
19


Reviews:
[{"review_id": "c73y8uK8EQ", "reviewer": "Reviewer_T9fC", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes Maverick-Shapley, a variant of Shapley value estimation for federated learning.\nIt quantifies each client’s contribution based on weighted per-class accuracy, where the weights are determined by class difficulty. The class difficulty is obtained by normalizing the class accuracies.\nThis approach is designed to address situations where “Maverick” clients, who hold rare or unique classes, are undervalued under standard Shapley-based mechanisms.\nThe method is further extended into a client selection strategy called FedMS and evaluated on MNIST and CIFAR-10.\n\n## Strengths\n- The motivation is clear (standard shapley undervalues rare-class clients in principle) \n- Solution is simple and straightforward \n- The paper is written very clearly\n- I especially like the experiments with adversarial clients, which can illustrate the robustness of the proposed approach \n\n## Weakness\n### Strong assumptions\n- Validation dataset assumptions: The approach relies on a balanced and representative validation set. If the validation dataset is biased or non-representative, the class-difficulty weights may be misleading as the class-difficulty weight depends on the accuracy. \n- Nature of class imbalance: This paper essentially addresses class imbalance. In federated learning, there are two main types:\n-- Within-client imbalance – each client has a skewed class distribution (e.g., ratio of positive to negative is 1:10).\n-- Cross-client imbalance – one or a few clients hold all or most samples of a single class, while others hold the rest.\nThis paper focuses on case (2). In practice, this is a very strong assumption: for example, it is rare for a hospital to only have patients with a single rare disease, unless it is a specialized clinic. I would appreciate if the authors could justify the realism of this problem setting with evidence or references.\n### Realism and Evaluation limitations:\n- Scope of applicability: Maverick-Shapley is most helpful in case (2), where one client owns a unique class. In case (1), the benefit is less clear, since all classes are already present across clients.\n- Alternative strategies: In the motivating scenarios (rare disease or rare accent), one could question whether federated learning is the right solution. Training specialized models for rare groups may sometimes be preferable, unless the explicit goal is to produce a single global model.\n- Dataset realism: The paper demonstrates performance only on MNIST and CIFAR-10. Its effectiveness on real-world rare-class scenarios is therefore less justified.\n### Results\n- The results section feels monotonous and a bit repetitive— Figures 2, 3, 4, and 5 present similar trends. It is difficult to judge whether the model should allocate more weight to Mavericks or non-Mavericks, the presentation style makes the trends look very similar across methods and heterogeneity levels. If possible, the authors should provide confidence intervals to illustrate the statistical significance of the Shapley reward results between Mavericks and Non-Mavericks.\n### Claim and evidence\nClarity of claims: Several statements in the paper are vague and would benefit from clarification. For example:\n“When examining the SR, we observe from Figure 3 that FedMS allocates more rewards to Mavericks compared to non-Mavericks in the IID setting, in line with the observed accuracy benefit of training with the Mavericks.” → How exactly is this **in line** with the observed accuracy benefit? Please explain the causal link.\n“Our proposed FedMS assigns equal importance to all classes and distributes the SR proportionally based on each client’s class contribution.” → This conclusion is not obvious from Figure 5a. Please justify how the figure supports this statement.\n## Summary\nWhile the motivation is clear and the empirical results are promising, the realism of the simulated scenarios, the reliance on strong validation set assumptions, the lack of clarification for some of the statements made in this paper, and the limited evaluation on real-world data raise concerns. Addressing these issues would significantly strengthen the paper and broaden its applicability", "claims_and_evidence_yn": "No", "claims_explanation": "Please see the weakness section", "audience_yn": "Yes", "audience_explanation": "Interesting problem. However, I would like to see whether the exact scenario the authors simulate using MNIST and CIFAR10 to validate the proposed approach actually matches real-world scenarios.", "requested_changes": "See above", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Bvssu41O0h", "reviewer": "Reviewer_ku3e", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper addresses a key challenge in Federated Learning (FL) and Shapley Value (SV): fairly evaluating the contributions of clients, particularly those holding rare or underrepresented data (referred to as Mavericks). Existing SV-based methods rely on uniformly weighted validation accuracy, which systematically undervalues Mavericks because rare classes are harder to learn and typically achieve lower accuracy. To address this issue, the paper proposes a Maverick-aware client selection algorithm that prioritizes clients based on their Maverick-Shapley contribution scores. The method adaptively selects clients and forms a coreset to maximize class-wise validation accuracy.", "claims_and_evidence_yn": "No", "claims_explanation": "The submission provides clear and well-documented empirical evidence that FedMS improves model accuracy, better aligns Shapley rewards with true contributions, and reduces adversarial and free-rider participation on MNIST and CIFAR-10 under synthetic Maverick scenarios. The algorithms are precisely specified, and results are reported with mean ± standard deviation across multiple baselines.\n\n\nHowever, the evidence is only partially convincing due to its narrow scope. \n\n1. The experiments are limited to small datasets such as MNIST and CIFAR-10, lack diversity in experimental settings, and do not include a scalability analysis. \n\n2. The approach also assumes access to a central validation set containing all classes, which may be unrealistic in real-world FL deployments. \n\n3. Moreover, the claim that the method is “private” is not well supported. Federated learning itself, as well as Shapley value evaluation, are not inherently privacy-preserving [A, B]. To substantiate this claim, one would need to demonstrate resistance to privacy attacks or integrate privacy-enhancing mechanisms such as differential privacy.\n\nOverall, the claims are supported within the reported experimental setting, but the work would benefit from broader and more rigorous evaluation to be fully compelling.\n\n\n[A] Boenisch et al., Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation, IEEE S&P 2023.\n\n\n[B] Wang et al., Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation, NeurIPS 2023.", "audience_yn": "Yes", "audience_explanation": "The investigated question about federated learning and data shapley value is of the interest of TMLR's audience", "requested_changes": "1.Include larger and more diverse datasets, test with more clients and classes, and report runtime and communication overhead. Moreover, conducting experiments in a fully decentralized setting with data heterogeneity would further strengthen the results.\n\n2. Discuss or propose alternatives to assuming a server-side validation set with full class coverage.\n\n\n3. Clarify privacy claims: either remove them or justify them with privacy attack–resilience evaluations or by integrating privacy-preserving mechanisms (e.g., membership inference attacks, differential privacy, secure aggregation).", "broader_impact_concerns": "NA", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "pyr1WxsPEF", "reviewer": "Reviewer_jgNt", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper investigates the problem of maverick-aware Shapley valuation (SV) in federated learning (FL). It argues that existing SV methods struggle to properly weight maverick clients, leading to suboptimal client selection and consequently weaker generalization performance. To address this limitation, the paper proposes using a class-wise recall ratio as the utility function in SV computations. Experiments conducted on the MNIST and CIFAR-10 datasets demonstrate the effectiveness of the proposed approach.", "claims_and_evidence_yn": "No", "claims_explanation": "Although the first claimed contribution is intuitive, it still lacks sufficient support. Additional empirical or theoretical evidence should be provided.", "audience_yn": "Yes", "audience_explanation": "It appears that maverick-aware Shapley valuation has not been extensively studied, yet it represents an important direction for FL.", "requested_changes": "I have several additional concerns:\n1. The first claimed contribution lacks sufficient supporting evidence.\n2. The proposed use of a class-wise recall ratio to address imbalance re-weighting among categories across clients is simple and straightforward, but may not be sufficiently novel.\n3. More recent federated learning approaches should be included for comparison.\n4. Equation (7) could be directly referred to as the recall ratio for clarity.", "broader_impact_concerns": "N.A.", "additional_comments": "I have several additional concerns:\n1. The first claimed contribution lacks sufficient supporting evidence.\n2. The proposed use of a class-wise recall ratio to address imbalance re-weighting among categories across clients is simple and straightforward, but may not be sufficiently novel.\n3. More recent federated learning approaches should be included for comparison.\n4. Equation (7) could be directly referred to as the recall ratio for clarity.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)