An Agnostic Approach to Federated Learning with Class Imbalance

1 INTRODUCTION . Class imbalance is ubiquitous in real world supervised learning problems . Examples span from medical applications ( Lee & Shin , 2020 ; Roy et al. , 2019 ; Choudhury et al. , 2019 ) , fraud detection ( Yang et al. , 2019 ; Chan et al. , 1999 ) , to consumer based applications ( Wang et al. , 2021b ; Wu et al. , 2020 ; Long et al. , 2020 ) . In these scenarios , data belonging to a subset of classes constitute a great proportion of the population while data from the minority classes , generated by uncommon events , are scarce ( He & Garcia , 2009 ) . Having a non-uniform number of samples per class deteriorates the performance of the classifier in the minority class ( Huang et al. , 2016 ) , resulting in low training and testing accuracy . More importantly , unintended consequences of mistreating the minority class can be catastrophic ( Van Hulse et al. , 2007 ) if the problem is not handled appropriately . From the perspectives of data heterogeneity and privacy , the issue of class imbalance is even more significant in the setting of Federated Learning ( FL ) . Due to the heterogeneity of the local data distributions , there can be a significant mismatch between the local and global imbalance , i.e . the class that is a minority locally can actually be a majority class globally . Moreover , for the purpose of privacy protection in FL , one should tackle the class-imbalance problem in an agnostic manner , i.e . the proposed algorithms should not require the minority class to be identified . In the centralized training setting , techniques like balanced sampling , loss re-weighting , and gradient tuning have achieved many successes ( Johnson & Khoshgoftaar , 2019 ) . However , due to the extra difficulty of handling the class imbalance problem in FL , previous arts that rely on the explicit identification of the minority class do not directly apply . While research along this line is quite limited , a commonly used heuristic is to estimate the portion of a class using the norm of the gradient per class . When used as proxies , these quantities are then utilized to reweight the losses corresponding to different classes ( Wang et al. , 2021a ; Yang et al. , 2020 ) . A key drawback of ( Wang et al. , 2021a ) is that a subset of the client ’ s local dataset needs to be shared , which is not ideal for privacy preserving purposes . Moreover , the effectiveness of such approaches degrades when there is a notable mismatch between the local imbalance and the global imbalance ( Wang et al. , 2021a ) . In this work , we target the most difficult yet possibly most interesting setting in FL , where there is a significant mismatch between the local and global imbalance . Concretely , on certain clients , a minority class from a global perspective is in the majority locally . Practitioners often encounters such a mismatch due to the highly heterogeneous data configuration in FL and the principle of locality ( Wang et al. , 2021a ) . For example , when the local data are produced by a minority user , the corresponding minority data could occupy a large portion of the local distribution . In this scenario , due to the scarceness of the minority data from a global perspective , the corresponding underrepresented client will usually experience poor service quality from the trained model , giving rise to potentially severe fairness issue . To overcome the aforementioned challenges , we propose a constrained learning formulation to handle the class imbalance issue in FL while accounting for both heterogeneity and privacy . In brief , we impose constraints on the standard FL formulation so that the empirical loss on every client should not overly exceed the average empirical loss . Such constraints are shown to force the classifier to account for all classes equally and hence mitigate the detrimental effect of class imbalance , under a type of heterogeneous data configuration that captures the mismatch between the local and global imbalance . The advantages of our formulation are threefold : First , in contrast to previous arts which usually rely on some heuristics to reweight the loss functions for different classses , our approach is principled , yielding a simple optimization interpretation . Second , unlike existing methods , our formulation requires no additional information compared to the original FL formulation and it treats data from different classes agnostically , and hence is less likely to leak the private information of the clients . Third , from our extensive empirical study , our formulation can significantly improve the testing accuracy on the minority class , without compromising the overall performance . Contribution . We summarize our contributions as follows . • Problem Formulation : To address the challenge of class imbalance in FL with data heterogeneity , we propose a novel constrained FL formulation with explicit enforcement on the similarity between the local empirical losses . Using only information from the standard FL objective , our approach is completely agnostic to class distribution of the client data and its proxies , as opposed to the existing literature ( which mostly violate privacy requirements of FL ) . • Meta-Algorithm : We solve our constrained optimization problem via a primal-dual approach . For a fixed dual variable , the corresponding Lagrangian function enjoys a similar structure as the standard FL loss , but with non-uniform weights on the local objectives . Accordingly , we propose an efficient ( meta- ) algorithm called CLIMB that can use any FL optimization method as a subroutine , with nearly negligible communication overhead . Furthermore , we analyze the convergence properties of CLIMB under certain oracle assumptions . • Accuracy Improvement in the minority class : On benchmark datasets , CLIMB with Fed-Avg as base solver achieves significant enhancement of the accuracy in the minority class without compromising the overall accuracy under various data heterogeneity and class imbalance scenarios . 2 RELATED WORKS . Class Imbalance in Centralized Setting . In the centralized learning setting , the number of samples per class is known . We categorize existing solutions that exploit such information as follows . Balanced sampling . In order to balance the data used for gradient calculation , the most common approaches are either to under sample from the majority classes ( Liu et al. , 2008 ) , or to augment the minority class ( Chawla et al. , 2002 ; Guo & Viktor , 2004 ) . Both methods seek to generate an artificial uniform distribution of data ( Buda et al. , 2018 ; Pouyanfar et al. , 2018 ) . Loss reweighting . Methods in this category focus on re-weighting the loss functions for different classes , with extra emphases on the mistakes made in the minority class ( Cui et al. , 2019 ; Ling & Sheng , 2008 ; Sun et al. , 2007 ; Wang et al. , 2016 ) . There also exist works like ( Lin et al. , 2017 ) that adjust the scale of the loss sample-wise , without exploiting the global data distribution . Gradient tuning . In the context of neural networks , some other works focus on tuning the gradient per class ( Anand et al. , 1993 ) showing faster rates of convergence in the class imbalance setting . To do this , the gradient is reshaped in order to have an equal magnitude when projected to all the gradients per class , while keeping the same norm as the original gradient . Class Imbalance in Federated Learning . Combating with the issue of class imbalance is more challenging in FL since the data composition in such a setting is generally unknown and the minority classes are often difficult to identify Li et al . ( 2020 ) ; Wang et al . ( 2020b ) . While research in this direction are quite limited , to the best of our knowledge , we classify them into the following two classes based on whether a proxy of the data composition is explicitly built . ( i ) A common strategy in existing methods is to explicitly build proxies of the data composition based on some heuristics . These proxies are dynamically adjusted during the learning procedure and allow the aforementioned three techniques , balanced sampling , loss re-weighting and gradient tuning , to be utilized in the FL setting : Wang et al . ( 2021a ) suggests that there exists a proportional relation between the magnitude of the gradients the corresponds to the last layer of the neural network and the sample quantity . Based on such observation , the proposed the Ratio-Loss , a class-wise re-weighted version of the standard cross entropy loss . We note that to define the Ratio-Loss , one needs to collect a subset of client data as “ auxiliary data ” , which may not be ideal in the setting of FL . On the same vein , Yang et al . ( 2020 ) argues that the gradient per class can be used as a proxy to infer the imbalances in the distributions of the clients , and thus clients should be selected according to how uniform the magnitude of gradient per class is . We also notice the work Astraea which introduces extra virtual components called mediators between the FL server and clients ( Duan et al. , 2019 ) . The mediator is assumed to have access to the local data distributions of the clients so that client rescheduling and data augmentation can be carried out accordingly . Through the prepossessing on the mediator , the gradients communicated to the server are made balanced class-wise . However , when privacy is taken into consideration , methods like Astraea may not be appropriate as in essence it is directly built on the global data distribution . ( ii ) Without explicitly constructing an estimation of the data composition , there are works that resort to techniques like active learning and reinforcement learning to implicitly learn the data composition as the optimization progresses . Works along this research line usually rely on client selection to mitigate the effects of class imbalances . In ( Goetz et al. , 2019 ) , to address the class imbalance problem , there will a higher probability to sampling clients that posses the global minority class . Other works , leverage client selection as a multi-armed bandit problem , and design a client selection policy to balance the gradients ( Xia et al. , 2020 ) . Other arts leverage Q-learning techniques to select clients in order to minimize the overall loss Wang et al . ( 2020a ) . However , we believe in the most practical setting of FL , clients that are available per communication round is incoercible and hence these strategies have limited applicability . Comparison with Previous Works . Our method significantly differs from the above strategies in the following ways : ( 1 ) Our method requires no knowledge of the data composition , nor any proxy of such information . Consequently , our approach is agnostic to the minority class and better preserves clients ’ privacy . Such a property draws clear distinction between our work and the works listed in class ( i ) above . ( 2 ) As will be more clear in the following section , we only perform clientwise re-weighting as opposed to the class-wise re-weighting schemes used in previous works , which further emphasize the agnostic nature of our approach . ( 3 ) In contrast to the methods in class ( ii ) above , our approach does not require active client selection , enjoying a broader applicability in the most practical and interesting settings .

Summary: The authors design a method, CLIMB, to solve the severe class imbalance issues in FL problem. In their method, they propose a constrained FL formulation (CFL) and adopt the method of Lagrange multipliers to solve this problem. They present some theoretical and experimental results to prove that their method is effective.