Title: A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments
Venue: NeurIPS 2025
Accepted: yes
Keywords: Model-heterogeneous federated learning

Fair federated learning

Inconsistent client participation
Abstract: Federated learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a shared model without exposing their raw data. However, existing FL research has primarily focused on optimizing learning performance based on the assumption of uniform client participation, with few studies delving into performance fairness under inconsistent client participation, particularly in model-heterogeneous FL environments. In view of this challenge, we propose PHP-FL, a novel model-heterogeneous FL method that explicitly addresses scenarios with varying client participation probabilities to enhance both model accuracy and performance fairness. Speciﬁcally, we introduce a Dual-End Aligned ensemble Learning (DEAL) module, where small auxiliary models on clients are used for dual-end knowledge alignment and local ensemble learning, effectively tackling model heterogeneity without a public dataset. Furthermore, to mitigate update conﬂicts caused by inconsistent participation probabilities, we propose an Importance-driven Selective Parameter Update (ISPU) module, which accurately updates critical local parameters based on training progress. Finally, we implement PHP-FL on a lightweight FL platform with heterogeneous clients across three different client participation patterns. Extensive experiments under heterogeneous settings and diverse client participation patterns demonstrate that PHP-FL achieves state-of-the-art performance in both accuracy and fairness. Our code is available at: https://github.com/Siyuan01/PHP-FL-main.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the challenge of inconsistent client participation in Federated Learning. The proposed approach consists of two key modules: the DEAL module, which employs a homogeneous auxiliary model to align heterogeneous local models and facilitate ensemble learning for improved local accuracy, and the ISPU module, which adaptively updates task-relevant parameters using an importance-based masking mechanism. The authors support their method with experimental results.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1: The paper addresses the issue of performance unfairness resulting from inconsistent client participation—a practical and important challenge in real-world federated learning.\n\n2: The proposed method leverages the DEAL and ISPU modules to guide learning in a way that improves both local accuracy and performance fairness across clients.\n\n3: The paper is well-written and easy to follow.\n\nWeakness:\n\nSee the Question section.\n\nQUESTIONS:\n1: In the introduction (line 27), the authors mention that it is impractical to assume all clients share an identical global model. However, the DEAL module still relies on a small homogeneous auxiliary model. Can this assumption be relaxed? If not, what are the key challenges in doing so?\n\n2: In Figure 3c, the values of $\\lambda$ for both datasets appear not to converge. Could the authors clarify whether convergence is expected?\n\n3: The paper lacks theoretical analysis. \n\nMinor:\n\n1: According to Definition 3, should the linear pattern plot in Figure 1c be sorted in ascending order to better reflect the defined metric?\n\n2: In line 127, the sentence is imprecise. The variable $p_i$ implicitly depends on other clients since it is defined as a function of $i$ (the client index), which may introduce cross-client dependencies.\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an approach for model-heterogeneous federated learning, with an aim to improve the fairness among clients considering the probabilities of their participations. The main contributions consists of two parts, one is the auxiliary model to transfer the knowledge among clients and the second is a selective parameter update mechanism.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1) The paper is well-organized and the presentation is easy to follow.\n 2) The designs of the two critical components have well-defined motivations. For the Dual-End Aligned Ensemble learning module, the integration of representation alignment and ensemble learning objectives facilitates both knowledge transfer and localized learning. For the Importance-Driven Selective Parameter Update module, the selection of important parameters for update aligns with the needs of adapting to varying participation frequencies among clients.\n3）The experiments are comprehensive. The method was compared with multiple state-of-the-art heterogeneous federated learning approaches. Specifically, three participation probability models were simulated to validate the effectiveness of the proposed method under diverse scenarios. Additional experiments, including results under model-homogeneous settings, are provided in the appendix.\n\nWeaknesses:\n1）As shown in Section 5.2, while there are some improvements, the gains over existing methods are not particularly significant.\n2）The method lacks theoretical analysis. For instance, there is no theoretical justification for why the selective parameter update mechanism adapts to different participation probability models. Intuitively, defining Equation (9) specifically for different participation probability models might yield better performance. Additionally, no theoretical analysis of convergence is provided.\n3） The proposed approach incurs notable additional computational overhead.\n\nQUESTIONS:\n1）Could using a fixed adaptability set to train the ensemble learning coefficients lead to overfitting, especially when these coefficients are retrained every round?\n2）How does the model perform under varying degrees of data distribution heterogeneity across clients? While the datasets used in the study already exhibit some heterogeneity, it would be valuable to clarify how the model performs under different levels of heterogeneity.\n3）In the experiments, are the performance metrics reported based on the local models of each client or the ensemble-averaged results after aggregation? This was not explicitly clarified in the text.\n4）K is used to represent the total number of clients and a tunable sharpness hyperparameter. I suggest to use two symbols.\n\nLIMITATIONS:\nI think the limitations addressed by the author are appropriate.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-15"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work addresses performance degradation due to nonuniform participation of clients in FL and proposes a new FL framework\ncalled PHP-FL in model heterogeneous FL environment that can improve model accuracy and performance fairness among clients.\n\nSTRENGTHS AND WEAKNESSES:\nThe proposed model can achieve good accuracy and performance fairness among clients.\nEach local client has an auxiliary model as well as its local model and trains both models,\nwhich obviously increases computational cost and communication cost. \nMasking information for each client is also uploaded, which also increases communication cost.\n\nQUESTIONS:\n1. One main concern is about the communication and computation cost to get and train auxiliary models \nfor clients. The cost also depends on the size of auxiliary models.\nIn the appendix the PHP-FL is compared with other auxiliary model-based models from the\nperspective of communication cost, but a comparison with non auxiliary model-based \nmodels is necessary from the perspective of both costs.\n\n2. The impact of masking needs to be investigated in detail. When the local dataset does not\ncontain full information on key features, masking based on the important parameters of\neach local model, may miss some key features. One important reason for the use of a global\nmodel is to utilize some missing key features, if any, for each local client. It seems that masking\nmay reduce the impact of the use of the global model.\n\n3. In eq. 2, backbone alignment and predictor alignment are considered through L_{MMD} and\nD_{KL}, respectively, but the scales of both terms may be different, so does it make sense to consider\nthe simple sum of both terms with different scales? \nIn eq. 3 how do we select the best for the customizable projection functions f and h?\n\n4. In Fig. 2, $M^h_\\*$ appears in the server, but no explanation is provided on it.\nIt seems to be a typo and hence is replaced by $M^h_i$ according to the expanation in section 4.1.\nIn step 6 of section 4.1, no explanation on how to update $M^h_i$ using $M^t_i$. Are they the same?\n\n5. In $L^w_{ENS}$ (eq. 4) the local model w is used in the first term and the second term,\nwhich seems to be overlapping. What happens if only the second term is considered?\n\n6. What model architecture is used in the experiments for the global model? Is it large enough to have\nfull trained information in it?\n\n7. In Fig. 3, it looks like that $\\lambda$ does not converge until communication round 100.\nThere is a possibility that more communication rounds would improve the performance by choosing a better $\\lambda$.\nA discussion on the convergence of $\\lambda$ would help understand the performance behavior.\n\n8. Improvement on unfairness due to frequently and infrequent active participation:\nThe PHP-FL can improve the fairness in accuracy as shown in the experiment. \nSince other auxiliary heterogeneous models also consider auxiliary models in training, what is the main\nreason why the PHP-FL outperforms other models?\n\nLIMITATIONS:\nThe proposed approach has relatively high communication and computational costs by considering auxiliary models \nfor clients.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the underexplored challenge of performance fairness in model-heterogeneous federated learning (MH-FL) under non-uniform client participation probabilities. The authors propose PHP-FL, which includes two key modules: (1) the Dual-End Aligned ensemble Learning (DEAL) module for bidirectional alignment between local and auxiliary models using MMD and KL loss, and (2) the Importance-driven Selective Parameter Update (ISPU) module, which adaptively updates task-relevant parameters based on participation frequency. Extensive experiments on Fashion-MNIST and CIFAR-10 under three participation patterns demonstrate that PHP-FL achieves strong performance in both accuracy and fairness, outperforming prior MH-FL baselines.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n\nThe paper tackles a timely and practical problem — performance unfairness under inconsistent participation in MH-FL — which has been insufficiently explored in prior work. This brings new perspectives to the FL fairness literature.\n\nThe DEAL module incorporates both representation-level (via MMD loss) and prediction-level (via KL divergence) alignment, and the ISPU module introduces a sigmoid-based update ratio to balance frequent and infrequent clients, both of which are thoughtfully designed and motivated.\n\nPHP-FL consistently outperforms state-of-the-art MH-FL baselines (FedAPEN, FedMRL, etc.) across multiple participation patterns and datasets, with improvements in both average accuracy and fairness. The ablation study validates the complementary effects of DEAL and ISPU.\n\nThe methodology is clearly presented with good visual illustrations (e.g., Fig. 2, Fig. 4), and code is released anonymously, enabling reproducibility.\n\n**Weaknesses**:\n\n- The experiments are only conducted on two relatively simple vision datasets (Fashion-MNIST and CIFAR-10). These may not sufficiently reflect the complexity and scale of real-world MH-FL applications. Adding results on at least one larger-scale or cross-domain dataset (e.g., CelebA, Reddit, or MIMIC-III) would significantly strengthen the empirical evaluation.\n\n\n- The proposed method introduces multiple additional components (auxiliary model training, MMD computation, adaptive masking), which may increase training cost and memory usage. However, there is no analysis of overhead (e.g., training time, FLOPs, memory footprint), nor comparison with lightweight alternatives. This may hinder practical deployment.\n\n- The paper designs three participation patterns (uniform, normal, linear), but these are synthetic and static. In real-world scenarios, client availability is often temporally correlated or bursty. The current evaluation may not reflect PHP-FL’s robustness under such dynamic conditions.\n\nQUESTIONS:\nSee weakness\n\nLIMITATIONS:\nYes (clearly acknowledged in Section 6 and the experiments)", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}