Accurate Forgetting for Heterogeneous Federated Continual Learning
Authors: Abudukelimu Wuerkaixi
Sen Cui
Jingfeng Zhang
Kunda Yan
Bo Han
Gang Niu
Lei Fang
Changshui Zhang
Masashi Sugiyama
Venue: ICLR 2024 poster (2024)
Keywords: federated learning
robustness
Primary area: societal considerations including fairness, safety, privacy
TL;DR: We propse the concept of accurate forgetting in federated continual learning to selectively utilize previous knowledge.

Abstract: Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under- explored. Bridging FL and continual learning (CL) gives rise to a challenging practical problem: federated continual learning (FCL). Existing research in FCL primarily focuses on mitigating the catastrophic forgetting issue of continual learning while collaborating with other clients. We argue that forgetting phenomena are not invariably detrimental. In this paper, we consider a more practical and challenging FCL setting characterized by potentially unrelated or even antagonistic data/tasks across different clients. In the FL scenario, statistical heterogeneity and data noise among clients may exhibit spurious correlations which result in biased feature learning. While existing CL strategies focus on the complete utilization of previous knowledge, we found that forgetting biased information was beneficial in our study. Therefore, we propose a new concept accurate forgetting (AF) and develop a novel generative-replay method AF-FCL that selectively utilizes previous knowledge in federated networks. We employ a probabilistic framework based on a normalizing flow model to quantify the credibility of previous knowledge. Comprehensive experiments affirm the superiority of our method over baselines.

Published as a conference paper at ICLR 2024
ACCURATE FORGETTING FOR HETEROGENEOUS FED- ERATED CONTINUAL LEARNING
Abudukelimu Wuerkaixi"
Sen Cui Jingfeng Zhang 2,3
Kunda Yan
Bo Han 433 Gang Niu 3 Lei Fang Changshui Zhang?
Masashi Sugiyama" 3,6
Institute for Artificial Intelligence, Tsinghua University (THUAI)
Beijing National Research Center for Information Science and Technology (BNRist) Department of Automation, Tsinghua University, Beijing, P.R.Chins
2 The University of Auckland RRIENN Hong Kong Baptist University
5 DataCanvas Technology Co., Ltd. 6 The University of Tokyo
ABSTRACT
Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under- explored. Bridging FL and continual learning (CL) gives rise to a challenging practical problem: federated continual learning (FCL). Existing research in FCL primarily focuses on mitigating the catastrophic forgetting issue of continual learn- ing while collaborating with other clients. We argue that forgetting phenomena are not invariably detrimental. In this paper, we consider a more practical and challenging FCL setting characterized by potentially unrelated or even antagonistic data/tasks across different clients. In the FL scenario, statistical heterogeneity and data noise among clients may exhibit spurious correlations which result biased feature learning. While existing CL. strategies focus on the complete utilization of previous knowledge, we found that forgetting biased information was beneficial in study. Therefore, propose new concept accurate forgetting (AF) and develop a novel generative-replay method AF-FCL that selectively utilizes previ- ous knowledge in federated networks. We employ a probabilistic framework based on a normalizing flow model to quantify the credibility of previous knowledge. Comprehensive experiments affirm the superiority of our method over baselines.
INTRODUCTION
Continual learning a learning scenario where model tries to learn series of new arriving tasks and maintain performance on old tasks (Thrun) 1994] Kumar & Daume III 2012 Li & Hoiem 2016: Jeon et al. 2023). This approach, inspired by human lifelong learning, is central to advancing the development of artificial general intelligence. Since birth, person would gather experience about real world by constantly learning various tasks and remembering them. Humans not only accumulate knowledge through self-directed learning but also collaboratively learn from others. However, concerns about data privacy and communication overhead arise when cooperating with others. Federated learning, which has attracted significant interests and gained various applications in industry (McMahan et al.) 20117 Yang et al. 2019: Li et al., (02I). has been an alternative to addressing these concerns. This leads the concept of federated continual learning (FCL) (Qi et al. 2023 incorporating continual learning into federated learning.
In FCL, the goal is that clients learn models for their private sequential tasks collaboratively without violating the data privacy of individual clients. This could encounter challenges from three fronts. One is statistical heterogeneity due to non-IID data across local clients. Such heterogeneity could severely degrade performance (Qu et al. (022) when learning from clients collahoratively. Another is catastrophic forgetting, stemming from restricted access to data from previous tasks due to realistic factors such as storage constraints, privacy issues, etc (Wang et al. 2023). This can lead the model to lose its ability to perform previous tasks proficiently after assimilating new tasks. There are
'Tese authors contributed equally to this work.
*Corresponding authors Code is https: // i thub. wttp.cccoomooccca66//cccccc

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few studies seeking to address the above two problems in FCL. For example, Usmanova al. (2021) extended the Learning without Forgetting (Li & Hoiem 2016) method to the FCL scenario, memorizing previous tasks among all clients. The third concern is associated with the potential introduction of feature bias resulting from the federated scenario, which in turn could impact the memory within CL models. Research indicates that the memorization of noisy labels can significantly impair the model's performance (Han et al. 2020).
Erronsous knowledge
Thorax Diseases
Liver Diseases
Hospital C
Benugn
Biasod Seotum
Memory
Thorax Diseases
Brain Diseases
Hospital B
Server
Classifier
Forpetting is invariably detrimental
Porformance Number
Brain Diseases
Heart Diseuses
emonecus feetures
Hospital
Number of memorired Feetures
Task1
Task2
Illustration of the FCL problem. Multiple hospitals within a federated learning network engage in the sequential acquisition of disease prediction tasks. The global memory bank, a crucial tool for the classifier in mitigating catastrophic forgetting, may possess biased features arising from statistical heterogeneity. Notably, the overall performance of the classifier could suffer degradation without strategic forgetting (The experimental verification is in Sec.
Figure 1:
Existing research developing FCL methods mainly assumed that thorough memorization of previous
tasks yields overall performance benefits (Usmanova et al., 2021 Qi et al. 2023). Elaborate strategies employed to memorize previous information (Yoon al.
202 Liu al. 2023). In practice, feature bias typically exists in the dataset, especially when there are lots of clients within the federated network. Because of such statistical heterogeneity, biased or even harmful information from particular clients may reside in the memory bank (i.e., memory buffer, generative models or model parameters) as shown in Figure []] The federated model may inadvertently learn to identify and rely upon spurious correlations arising from diverse tasks among multiple clients. Furthermore, the model may integrate label noise (Zhang et al. 2024) introduced by few clients. For example, in a federated learning system implemented among hospitals nationwide, these medical institutions may encounter varying disease profiles over time. Besides, hospitals located at distinct geographical areas often cater to diverse distributions as depicted in Figure] T Therefore, strategically mitigating erroneous knowledge during the acquisition of new tasks is required.
Motivated by the phenomenon in reality that the new arriving tasks of each client may not be correlated, we consider a more practical and challenging FCL setting in this paper: limitless task pool (LTP). From temporal perspective, the tasks that a single client randomly selects from the LTP various time points might be unrelated or even antagonistic, thereby presenting a significant challenge for model learning. To overcome the problem, we propose novel generation- Forgetting Federated Continual Learning (AF-FCL) We argue that the forgetting phenomena are not invariably detrimental (Han et al. 2020). Conversely, accurate forgetting mitigates the negative impact of the heterogeneity on model learning.
method Accurate
Instead of learning generative adversarial network (GAN) for indiscriminate generative-replay in existing FCL methods (Qi et al. 2023). AF-F FCL aims to facilitate a selective utilization of previous knowledge through correlation estimation. order to accurately identify benign knowledge from pre- vious tasks, we achieve correlation estimation with learned normalizing flow (NF) model (Durkan) jet al. 2019; Winkler et al. 2019 Rezende & Mohamed 2015) in feature space. Specifically, an NF model could map an arbitrarily complex data distribution to pre-defined distribution through sequence of bijective transformations. Such invertability enables the NF to have a lossless memory of the input knowledge and accurately estimate the probability density of observed data. While the infor- mation the NF model could contain biased features or spurious correlation due to heterogeneous data, we suggest outlier features with respect to the current tasks suspicious and may pose threat
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to the learning process. More precisely, the credibility of particular feature could be quantified with its probability density in the current tasks.
Experimental results corroborate that AL significantly outperforms all baselines on a series of benchmark datasets. We summarize our key contributions as follows:
We consider a more practical and challenging setting. We suggest the harm of remem- bering biased or irrelevant features, which could be unavoidable in the federated scenario due to statistical heterogeneity.
We propose the concept accurate forgetting and develop a novel generative method, AF-FCL. It adaptively mitigates erroneous information by correlation estimation with an NF model.
We conduct extensive experiments a series of benchmark datasets. The results with ablation studies demonstrate the effectiveness and superiority of our proposed accurate forgetting over existing state-of-the-ar methods.
2 RELATED WORK
Continual Learning. Continual learning has witnessed the development of diverse methodolo-
gies (L.ange al.] 2022), which can be roughly divided into three families: (I) Regularization-basee methods: LwF employs the knowledge distillation loss, where the previous model's output is uti- lized as soft labels for the current tasks when working with new data (Li & Hoiem, 2016). Stable
SGD MMizadee et al. 2020) demonstrated performance enhancements by calibrating pivotal hyper- parameters and systematically reducing the learning rate upon the arrival of each task. (II) Parameter isolation methods: |uusu et al. (2016) suggested augmentation of the model with new branches tailored incoming tasks. (III) Replay-based methods: Generative replay-based methods use an auxiliary generator to model the data distribution of acquired knowledge, producing synthetic data for replay in instances (Odena al. 2017 Wu al. 2018). While existing research predominantly focused on the efficient memorization of past knowledge, we turn our attention to a more foundational question: prior knowledge perpetually beneficial?
Federated Learning. Federated learning represents distributed learning paradigm among multiple clients and central server. Researchers have been endeavoring to address the statistical heterogeneity by developing a comprehensive global model (Wang et al. (222)). Mohri et al. (2019) aimed to achieve a fair distribution of model performance by optimizing its efficacy across any given target distribution. Zhu et al. (2021b) suggested the utilization of generator to aggregate user information. This, in turn, guides the local training by employing the acquired knowledge as an inductive bias. In this work, we consider more challenging learning problem associated with statistical heterogeneity in federated scenarios: how to facilitate collaboration when all clients are tackling different tasks?
Federated Continual Learning. To date, there are few studies in the domain of federated continual learning. Casado et al. (2020) studied the scenario of data distributions changing over time in federated learning. Federated reconnaissance presented scenario with incrementally new classes during training and proposed to utilize prototype networks (Hendryx et al. 2021). Guo et al.) (2021) proposed a regularization-Dasee algorithm and new theoretical framework for it Usmanova et al. (2021) presented a distillation-basse method to deal with catastrophic forgetting, using previous model and global model as teachers for the training of local models. Yoon al. (20216) proposed a novel parameter isolation method for the federated diagram, where the network weights are decomposed into global parameters and task -sseiiiec parameters. Dong et al.) (2022) considered federated class- incremental setting and developed distillation-basse method to alleviate catastrophic forgetting from both local and global perspectives. Qi al.) (2023) customized the generative replay based method ACGAN with model consolidation and consistency enforcement. Our method considers the issue of memorizing biased feature due to statistical heterogeneity, exhibiting notable differences compared to the aforementioned methods.
PROBLEM DEFINITION
3.1 NOTATIONS
Continual Learning. In standard continual learning scenario, there are a sequence of tasks (T+, ...,, "T.,, ,T"), where 7 is the number of tasks, and T is the i-th task. Each dataset is composed
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of n' pairs of data and labels: D' { Vk J2-1- When learning on the t-th task, one has direct access to previous data D I' The goal of continual learning is effectively manage the current task while preserving its performance on all previous tasks:
min [C(('; ( L(O'; TT),, C( T*),
where C is the risk objective of tasks and 0 is the model parameters learned on the t-th task.
Federated Learning and Statistical Heterogeneity. In federated learning scenario, there are N clients, and each client owns a private dataset. The goal of federated learning is collaboratively learning models without accessing the datasets belonging consists of the imput space X; and output space Y,, where X; and Y; are shared across all clients. There are nt, samples in the i-th client denoted as { ( yk 1 k=1" Different clients may exhibit non-identical joint distributions p(z, y) of features and labels, i.c., p( Ut.) # p(Itz V62). where i # ig.
the local clients. The data of clients
3.2 FEDERATED CONTINUAL LEARNING
FCL refers to practical learning scenario that melds the principles of federated learning and continual learning. Suppose there are N clients, and each client possesses a private series of datasets {D (i= Please note that, at a given step client & can only have access to DÍ as in continual learning, In existing literature, the primary focus is on a specific task reshufiling setting, wherein the task set is identical for all users, yet the arrival sequence of tasks differs (Yoon et al. 2021a). In practical scenarios, it may be observed that the task set of clients is not necessarily correlated. Thus we consider practical setting, the limitless task pool (LTP), denoted T. For each client, the dataset Dj. of the k-th client at step / corresponds to a particular learning task Th CT. There is no guaranteed relation among the tasks (Tx ....... Th") in the k-th client at different steps. Similarly, at step t, there could be no relation among the tasks (Ti, T ....; TV across different clients.
Limitless Task Pool. In the setting of LTP, tasks are selected randomly from a substantial repos- itory of tasks, creating a situation where two clients may not share any common tasks, i.e., (T))ee n (T
1== 2 0, P.q = 1.2.... N. More importantly. clients possess diverse joint distributions of data and labels p(x, y) due to statistical heterogeneity. Therefore, features learned from other clients could invariably introduce bias when applied to the current task.
Biased Features. The bias originating from particular client can adversely affect the performance of the model across different clients and a range of tasks. We tackle a more practical and challenging FCL problem that differs from the task reshuffling setting (Yoon et al. 202 la) from two perspectives: (I) For different steps, tasks allocated to each client are randomly drawn from an extensive task pool. (II) For different clients, tasks across various clients may be unrelated or even contradictory in each step, consequently amplifying bias during the learning process.
Our goal is to facilitate the collaborative construction of the global model with parameters 0. Under the privacy constraint inherent in federated learning and continual learning, we aim to harmoniously learn current tasks while preserving performance on previous tasks for all clients, thereby seeking to optimize performance across all tasks seen so far by all clients, i.c.,
min [s] S ................................ S...... s.),,,)), where S. = [C(0', L(0'; d'
C(O':
(2)
4 VALIDATION OF ACCURATE FORGETTING
In this section, we present the results on a noisy dataset to intuitively demonstrate the effectiveness of motivation and approach.
4.1 DATASET WITH LABEL NOISE
We argue that forgetting is not invariably detrimental within the realm of FCL and propose the concept of accurate forgetting. To validate our argument and the efficacy of our proposed method, we curate the EMNIST-noisy dataset, wherein subset of noisy clients is simulated by introducing random labels to the data. Additionally, we acknowledge the presence of noise in practical datasets, notably in the form of label noise.
As a character image dataset, the EMNISTTnoisy dataset comprises 8 clients, each encompassing tasks, with each task containing 2 classes of character images. We randomly select several clients and
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assign random labels for their initial three tasks, as displayed in Figure 2(a) These incorrect labels have the potential to propagate adverse effects, affecting subsequent task learning across different clients through the memory bank. After learning sequentially all tasks, we evaluate the final three tasks, which do not contain any noisy labels. This evaluation allows us to exclusively assess the impact of incorporating noisy information from previous tasks into the memory bank.
4.2 RESULTS
The baselines in Figure 2(b) are representative CL and FCL methods. It is observed that: (I) the performance of the baselines demonstrates inferiority compared to the naive FedAvg method; (II) the performance of the baselines suffers a rapid deterioration with an increasing number of noisy clients.
These CL and FCL baseline methods are meticulously designed to effectively retain knowledge from previous tasks. However, the presence of noisy clients introduces harmful information into the model learning process. The memorization of such erroneous information proves detrimental to the overall performance. Consequently, the baselines exhibit suboptimal performance compared FL method, which does not employ explicit memorization techniques. In contrast, our approach incorporates adaptive mechanisms to mitigate the impact of erroneous information. By effectively alleviating the adverse influence of noisy clients, our method consistently surpasses all baselines. Notably, the performance of our method maintains relative stability even with an increasing number of noisy clients in the dataset.
Fal AI-ILZ
k
8
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Client3
K
4
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S
....
9
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Z
Label Naig:
n
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FedAvg
Client2
y
b
t
J
O
40
h
n
u
Client1
f
h
h
S
V
Task
Task 2
Task 4
Task 5
Number of malicious elients
(a) Diagram of the dataset
(b) Accuracy of methods
Figure 2: Illustration of the EMNIST-noisy dataset and results. (a) The initial several tasks in Client2 exhibit label noise. (b) The average accuracy of methods is presented with respect to increasing number of malicious clients. The baseline methods are illustrated by dash-dotted lines, while our method is depicted with solid line.
METHODOLOGY
5.1 PRELIMINARY: NORMALIZING FLOW
Normalizing flow a type of generative model. It able map complex, multi-modal distribution to a simple probability distribution such as standard Gaussian distribution through a sequence of smooth and invertible transformations (Rezende & Mohamed 2015). In particular, an NF model is diffeomorphism composed of a series of invertible transformations g 91 .... gk. of which a widely applied transformation is affine coupling layer (Kingma & Dhariwal 2018).
Lossless Memory. Through meticulous design of the invertible layers, normalizing flow accomplishes a bijective transformation, preserving the one-to-one correspondence between the elements of the input and output spaces. The bijectivity ensures lossless memory of the original input. Consequently, this inherent property of NF is pivotal in enabling the accurate modeling of complex distributions, and stands central in generative applications.
Exact Likelihood Estimation.
The invertibility enables precise estimation of the probability density
of data samples within the learned dataset. Specifically, with target dataset Z = {zj ) Rd and prior distribution (u), u E Rd, an NF model learns the diffeomorphism g with the parameters that maps dataset distribution p= to the prior: u = {=)). Under above transformation, the probability density of the given datapoint can be computed as:
du
0g
log (2) = log (w) -log det 0z
= log logP)(((2)) +
log det
(3)
Og'
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Probabiitty of generated features Pot (i,)
NF model
g
(w)
Rac
X
L
Normal distribution in
latent space
3
Lie
Lets
D
Ra
ha(x)
Abc
112
LxD
Old feature extractor KG
(x)
The diagram of training the classifier locally with our method. The training objective consists of three integral components: (I) Li representing the objective for training using features generated and estimated probabilities derived from the global NF model: (II) L corresponding to
Figure 3:
the objective for training using original data; (III) LKD. which denotes the objective for knowledge distillation within the feature space.
where o' denotes input of the I-th transformation of NF model. The transformations of the NF model are deliberately crafted to facilitate efficient computation of their Jacobian determinants det 8g
0g'
A conditional NF model can take label y as conditional information in likelihood estimation Ps (z, y).
The Training of NF Models. The training objective of NF model is also derived from Eq. trained to maximize the likelihood of samples from target dataset Z, i.e.,
n
Σ log det dg. ++1 Og,
)
CNF(g: 2)
log logpa(z))
logp)(9(2)))
(4)
5.2 AN OVERVIEW OF AF-FCL
In FCL, statistical heterogeneity among clients brings extra challenges for continuously learning sequence of tasks. Especially LTP setting, particular clients could possess unrelated tasks and biased dataset. When bias spurious correlation from particular clients is memorized by the model, a decline in model performance may occur in the task sequences of all clients. Therefore, a direct deployment of continual learning methods designed to mitigate catastrophic forgetting is hard to address the heterogeneity issues in FCL.
We propose novel method AF-FCL, which adaptively utilizes memorized knowledge and learns unbiased feature for all clients under the FedAvg framework (McMahan et al.) 2017). The training schematic of the classifier in each client is illustrated in Figure 3 Overall, the implementation of
AF-FCL consists of the following components: (I) feature generative-replay To prevent complete forgetting, we train global NF model the feature space of classifier for generative replay. (II) knowledge distillation. Additionally, we employ knowledge distillation in the feature space to mitigate significant drift, thereby enhaneing the stability of the training process for the NF model. (III) correlation estimation. We suggest that features exhibiting outlier characteristics with respect to the current tasks can potentially undermine the learning process. Therefore, we assess the reliability of the generated feature by its probability density within the current tasks.
5.3 ACCURATE FORGETTING FOR HETEROGENEOUS FCL
The above See 5.2
5.25.2 gives an overview of our method. In this section, we provide a detailed description of AF-FCL and how it is implemented.
Generative-replay in Feature Space. We consider the classification tasks, where we need to train classifier with L layers: h (h1, h2 h2). We split the classifier into three sub-modules: h2 = {h1, fu....,, h ho = [h441...... h he = {hz). The h2 and h2 are two successive feature extractors, he is the classifier head. To maintain the performance on previous tasks, we train conditional normalizing flow model g in the feature space, which is the output space of hg. In this way, the normalizing flow model retains the feature of previous tasks. The NF model g is trained globally with FedAvg algorithm using client datasets and sampled data:
ENF(g: Dj. G2) = [
1
Σ DE
log Pa (ha Oog
y1)
Σ Bi
(o.. (5)
|G

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where Di is the dataset of the t-th task in the k-th client, and p= is the likelihood calculated in 3 G2 is the feature set sampled from NF model g' (g/ is the stored NF model after training on the last task), so that the current NF model avoids forgetting previous features.
Normalizing flows operate within a latent space that maintains dimensional parity with the target data space. Training the NF model in high-dimensional data space X could be computationally intensive. Furthermore, the inherent sparsity of raw data can hinder the NF model's capacity to obtain representative sample of the data distribution (Brehmer & Cranmer 2020). Therefore, we train the NF model in the compact, low-dimensional feature space as opposed to the data space, thereby reducing the complexity of generation.
We also leverage the feature space to extract more robust semantic information.
Knowledge Distillation for Consistent Feature Distribution. The NF model is trained in the feature space of classifier to maintain previous knowledge. The NF model retains knowledge from previous tasks, conveying it to the classifier via feature generation. Yet, feature extractor of the classifier undergoes continual modifications throughout the training process. If the feature space of the classifier drifts significantly, the knowledge memorized by the NF model may become obsolete.
Therefore, the feature space of the classifier needs to retain relative consistency during the training. We propose to apply knowledge distillation in the feature space of the classifier to control the drift of feature distribution:
n LkD(h; D2) = Σ
(1:
- (x:)),
ni 1=1
where fi is the stored classifier feature extractor after training on the last task.
Correlation Estimation for Accurate Forgetting. From the above, train the classifier with the aid of NF model by generating features. However, utilizing previous knowledge without discrimination may lead to biased model stated before. Thus we propose accurately exploit the memorized knowledge with the characteristics of the NF model for correlation estimation. In particular, when training the classifier for the 1-th task of client k, we firstly map the feature of local data the latent
space of normalizing flow, i.e., 02 = {u; g ((h. (1:))) the features to a disentangled latent space, which is the centered isotropic multivariate Gaussian. Therefore, approximate the true distribution Ug in each class multivariate Gaussian with diagonal covariance structure. The mean vector u4 and covariance matrix Σ of Üg can be easily computed by
Ii E Dj. As the NF models transform
diag(u: - m4)) diag(1 wh), us C
11
n
where diag(u) turns the vector into diagonal matrix.
For generative replay, we sample batch of latent vectors the NF model and project them feature space: Ug = { ūū 2; = g (u,), T, J.]--1- ū, E Pa. Please note that we use bar superscripts to denote generated data. The generated features from NF model represent the knowledge of previous tasks among all clients. However, in FCL scenario, there may exist irrelevant or even biased feature from other clients due to statistical heterogeneity. Enhancing the memorizing of biased feature could cause subpar performance or even failing to converge. Considering that outlier features with respect to the current tasks could be unreliable, we quantify the credibility of generated feature with its relevance to local dataset. To evaluate the correlation between the generated feature and the current task, propose to use the the probability density of the sampled latent vector ú; within the current feature distribution quantified in 7 i.e.
(2 cxp (- - -4) m4)
PD (ū:)
(8)
The probability PDt (ū.) above quantifies the degree of correlation between the current task in the local client and the sampled features from NF model. We use the correlation probability of the generated features re- reighttht the loss objective L% And the final objective Lats consisting of three
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terms is follows:
5 (h; Di))
Σ k
ce (h(r))) U:),
C Ce Ug)
1
E PDt Lce (ho,cl (() Dt).
(9)
n1
6,EU,
Loi Di Ug) -Lae (h; Dk) C (h; (k D2)
where Loe (h; D denotes the cross-entropy loss of raw dataset, and (h; Ug) denotes the unbiased objective of generated data. With the proposed method, the classifier learns beneficial features from previous tasks and accurately forgetting biased features. Moreover, the NF model memorizes more benign features. Both the NF model and the classifier expected to be of increasing generalizability with the advancement of training progress. The implementation of AF-FCI is in Algorithm
EXPERIMENTS
6.1 EXPERIMENTAL SETTINGS AND EVALUATIONS
Datasets and Settings. We curate three FCL datasets with different settings. We N denote the number of clients, to denote the number of tasks in each client, C to denote the number of classes in each task. For the EMNIST-based dataset containing 26 classes of handwritten letter images (Cohen et al. 2017). we set the following two settings with N=8, T'=6, C=2. 1) EMNIST- LTP: in LTP setting, we randomly sampled classes from the entire dataset for each client. 2) EMNIST-shufffe in conventional shuffle setting, the task sets are consistent across all clients, while arranged in different orders. 3) CIFAR100: We randomly sample 20 classes among 100 classes of CIFAR100 (Krizhessky et al. 2009) as task for each of the 10 clients, and there are 4 tasks for each client (N = 10, 4, C' 20). MNIST-SVHN-F: We set 10 clients with this mixed dataset. Each client contains 6 tasks, and each task has 3 classes.
Metrics. We use the metrics accuracy and average forgetting for evaluation following recent works (Mirzadeh al. 2021 Yoon et al. 202 Average forgetting assesses the extend of backward transfer during continual learning, quantified as the disparity between the peak accuracy and the ending accuracy of each task.
6.2 BASELINES
We compare our method AF-FCL with baselines from FL, CL and FCL. In FL, we consider two representative models FedAvg (McMahan et al. (017) and FedProx eti al. 2020p. In CL. PODNet incorporates a spatial-based based distillation loss onto the feature maps of the classifier (Douillard et al.. 2020). ACGAN-Repla, employs GAN-based generative replay method (Wu et 2018). The CL models are respectively combined with the FL models. In FCL, FLwF2T leverages the concept of knowledge distillation within the framework of federated learning (Usmanova et al. 2021 FedCIL extends the ACGAN ACGAN-Replay method within the federated scenario (Qi et al. 2023). GLFC exploits a distillation-basel method to alleviate the issue of catastrophic forgetting from both local and global perspectives (Dong et al. 2022).
Table 1: Average accuracy and forgetting on EMNIST-LTP and EMNIST-shuffle dataset.
EMNIST-LTP
EMNIST
Model
Accuracyt
Forgetting
Accuracyt
Forgetting
FedAvg
32.540.9
20.8
70.3
4.940.6
FedProx
35.3
19.2+0.6
69.4
6.0+
PODNet#FedAvg
36.9
19.8
71.0=a.4
3.9
PODNet+FeeProx
40.4
14.3
70.6
9.6
ACGAN- ACGAN-Reepla--FeedAAg
38.4+0.2
9.8+0.8
70.0
4.7+0.3
ACGAN-Replay-Foddttx
41.3+0.9
10.4+0.7
70.3+1.2
6.1+2.0
FLwF2T
40.1
15.5
71.0=0.9
8.1
FedCIL
42.040.6
12.4+0.3
71.120.4
6.440.2
GLFC
40.1
14.3
74.9
5.6
AF-PCL
47.5+0.3
7.940.5
75.8
4.2+
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6.3 EXPERIMENTS ON EMNIST- BASED DATASETS
EMNIST-LTP. In this dataset, clients may encompass unrelated tasks, thus rendering the dataset challenging, As the results shown in Table I some of the CL methods integrated with FL algorithms demonstrate comparable performance to that of FCL methods in the EMNIST-LTP dataset. For instance, the average accuracy of CCGAN-Replay++erree FLwF2T and GLFC. This phenomenon can be attributed to challenge posed by the elevated degree of heterogeneity under the LTP setting, which difficult for these FCL methods to deal with, consequently diminishing their inherent advantages. Nevertheless, our method outperforms all the baselines in the EMNIST-LTP dataset. We argue that statistical heterogeneity in federated networks inevitably results in biased information residing in the memory bank. Both CL methods and existing FCL methods assume that memorization is beneficial, potentially losing their advantages under LTP setting. Our method adopts accurate forgetting mitigate the negative impact of heterogeneity and selectively encourages the forgetting of malign information. It shows the highest accuracy rate and lowest forgetting rate.
41.3% higher than two FCL methods
Different from the EMNIST-LTP dataset, EMNIST-shuffle represents a more tractable dataset within the conventional setting, resulting in higher overall accuracy rates in Table The FCL methods exhibit superior accuracy compared to CL methods, underscoring their strength. And our method still showcases a superior capacity than all baselines in this commonly adopted dataset setting.
EMNIST-shuffle.
6.4 EXPERIMENTS ON MORE COMPLICATED DATASETS
CIFAR 100 comprises 100 classes of images. The composite dataset MNIST-SVHN-F comprises two distinct digit classification datasets: MNIST and SVHN, characterized by complex colors and backgrounds, along with clothing image classification dataset. Table displays the results of these two challenging datasets CIFAR 100 and MNIST-SVHN-F Different tasks exhibit reliance on varying features. For instance, shape features pertinent to digits differ significantly from those relevant to clothing classification. A naive collaboration among clients may lead to a model overly reliant on spurious correlations, overlooking the importance of task-specific features. We suggest strategy of selective utilization and memorization of learned feature. By relying on the generated features with a higher correlation, FF-FCL significantly exceeds the performance of baselines.
Table 2: Average accuracy and forgetting CIFARI00 and MNIST-SVHN-F dataset.
CIFAR100
MNIST-SVHN-F
Model
Accuracy!
Forgetting.
Accuracyt
Forgetting
FedAvg
26.3
8.441.2
55.7=1.4
21.9=0.9
FedProx
28.7
8.2
56.1
21.3
PODNet+FedAvg
30.5
8.641.7
54.2=0.8
20.6
PODNet+FedProx
32.5
6.440.4
56.4
20.0
ACGAN REGANN-ReplayFeedAAy
32.1
5.441.1
56.0
21.4-0.8
ACGAN- ACGAN-Replay+FeddPa
31.8+00.7
6.2+1.2
56.4-2.1
22.1+1.4
FLwF2T
30.2+0.7
7.2+1.8
54.2
25.6
FedCIL
33.5
6.5+1.0
57.2-1.7
19.7
GLFC
35.610.6
6.2
61.8
10.8
AF-FCL
36.3 36.3 10.4
4.9
68.1 68.1...9
7.5 1..0
7 CONCLUSION
In this study, we navigate the challenges of continual learning real-worke federated contexts, specifically when faced with data task streams that might be biased noisy across clients. Current research continual learning emphasizes the adverse consequences of "catastrophic forgetting". However, we advocate for perspective that reveals the merit of selective forgetting, especially as mechanism to mitigate the biased information induced by statistical heterogeneity in reality. Inspired by it, present generative framework, termed AF AF-FCL, meticulously crafted to achieve targeted forgetting by re- weighting generated features based on inferred correlations. The experimental results clearly demonstrate its effectiveness.

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Published as a conference paper at ICLR 2024
8 ACKNOWLEDGMENTS
This work is funded by the Natural Science Fundation China(NSFC. No. 62176132) and the Guogiang Institute of Tsinghua University, with Grant No. 2020GQG0005. MS was supported by JST CREST Grant Number JPMJCR18AL and a grant from Apple, Inc. Any views, opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and should not be interpreted as reflecting the views, policies or position, either expressed or implied, of Apple Inc. BH was supported by the NSFC General Program No. 62376235 and CCF-Baidu Open Fund.
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A DATASETS
We construct series of datasets comprising multiple federated clients, with each client possessing sequence of tasks. Suppose we use N to denote the number of clients, T to denote the number of tasks in each client, C' denote the number of classes in each task. We curate tasks by randomly selecting several classes from the datasets and sample part of the instances from these classes. Adhering to the principle of class incremental learning, there are no overlapped classes between any two tasks within a client.
EMNIST-LTP. The EMNIST dataset is a character classification dataset with 26 classes (Cohen et al. 2017). It contains 145600 instances of 26 English letters. The data contains both upper and lower case with the same label, making it more challenging for classification. To curate dataset under LTP setting, we randomly sampled classes from the entire dataset for each client. The EMNIST-LTT dataset consists of 8 clients, with each client encompassing 6 tasks, each task comprising classes (N = 8, = 6, C 2).
EMNIST--huffle. conventional reshuffling setting, the task sets are consistent across all clients, while arranged in different orders. Therefore, with the same structure as EMNIST-LTP, we construct EMNIST-shuffle dataset with 8 clients, tasks each, and each task comprising classes. While the tasks of all clients are the same but in shuffled orders.
EMNIST-noisy. In this paper, we argue that forgetting is not invariably detrimental in FCL and propose the concept of accurate forgetting. To validate our argument and effectiveness of our method, we curate the EMNIST-noisy dataset with a few malicious clients by assigning random labels to the data. Besides, there could be noise in realistic dataset, including label noise. And malicious clients with adversarial behavior should also be taken into consideration under cross-device setting in Federate Learning. Robustness of FCL methods is crucial in real-world application. The EMNIST- noisy possesses the same structure as EMNIST-LTP dataset (N = 8, T' = C.C = 2). We randomly selects several clients and assign random labels their first three tasks. After learning sequentially on all tasks, we evaluate on the last three tasks without noisy labels. By this means, we assess the impact of incorporating noisy information into the memory bank from previous tasks.
CIFAR100. As a challenging image classification dataset, CIFAR100 consists of low resolution images containing various objects and complex image backgrounds (Krizhevsky et al.) 2009). We randomly sample 20 classes among 100 classes of CIFAR 100 as task for each of the 10 clients, and there are 4 tasks for each client (N = 10, T 4, C 20). For each class, we randomly sample 400 instances into the client dataset.
The mixed dataset is constructed with MNIST (LeCun et al. 1998), SVHN (Netzrr et al. 2011) and FashionMNIST (Xiao et al.] 2017). Similar to MNIST, SVHN dataset serves as benchmark for digit classification tasks, notable for its representation of real- world scenarios with complex backgrounds. We unify the labels of these two datasets. NNST dataset is designed for clothing image classification. We set 10 clients in the mixed dataset, with each client containing 6 tasks, and each task has 3 classes. (N 10, T = 6, C 3). In this mixed dataset, different
MNIST-SVHN-EE
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tasks rely different features. For example, shape features that are relevant to digit classification differ significantly from those that are important for classifying clothing items. If clients collaborate naively, it may result in model that relies too heavily spurious correlations, thus neglecting the significance of task-specific features.
B BASELINES
We compare our method AF-FCL with baselines from FI., baselines from CL and three baselines from FCL.. The FL. methods simply train global model on sequential tasks, without any memorizing technique. The CL methods are respectively combined with the FL methods, training global model while fighting catastrophic forgetting. The FCL methods focus on addressing the issues of catastrophic forgetring along with statistical heterogeneity.
FedAvg |McMahan et al., 2017). As representative FL method, FedAvg trains the models in each client with local dataset and averages their parameters to attain global model.
FedProx (Li et al. 2020 The algorithm is similiar to FedAvg. While training local models,
regularization term employed to govern the proximity between the local parameters and the global parameters. This regularization term serves to effectively control the degree of deviation exhibited by the local models from the global model during the training process.
PODNet DDuilllrd et al. 2020). As CL method, the algorithm incorporates spatial-based distillation loss onto the feature maps the classifier. This loss term serves to encourage the local models to align their respective feature maps with those of the previous model, thereby maintaining the performance in previous tasks.
ACGAN-Replat This CL algorithm employs GAN-based generative replay method (Wu et al. 2018). The algorithm trains an ACGAN in the data space to memorize the distribution of previous tasks. While learning on new tasks, the classifier is trained new task data along with generated data from ACGAN.
FLwF2T. As FCL algorithm, FLwF2T leverages the concept of knowledge distillation within the framework of federated learning (Usmanova et al. 2021). It employs both the old classifier from previous task and global classifier from server train the local classifier.
FedCIL. The FCL algorithm extends the ACGAN- Replay method within the federated scenario, addressing the statistical heterogeneity issue with distillation loss (Qi et al.
2023).
GLFC. In FCL scenario, the algorithm exploits distillation-based method to alleviate the issue of catastrophic forgetting from both local and global perspectives (Dong et al. 2022).
C IMPLEMENTATION DETAILS
C.I ALGORITHM
The algorithm of our method detailed in Algorighm
C.2 METRICS
We use the metrics of accuracy and average forgetting for evaluation following recent works (Mirzadeh)
et al. 2021 Yoon et al. 2021a). Suppose dj is the test set accuracy of the i-th task after learning / th task in client
Average Accuracy. We evaluate the performance of the model on all tasks in all clients after it finish learning all tasks. By using a weighted average, we calculated the test set accuracy for all seen tasks across all clients, with the number of samples in each task serving the weights:
N
Average Accuracy
T
(10)
Em Ei=
k= t=1
This approach allows us to account for variations task difficulty and ensure a fair evaluation across different tasks and clients.
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Algorithm 1 Federated continual learning framework FF-FCL
Input: Datasets of tasks for clients {D1, D2...., Dv1. D3 = (Tp. ....... Tg) classifier and normalizing flow model g: for task = 1....... do
2: h'' + h; 9 <
3: for round r = ... do
4: Server randomly selects clients C for local training and send them model parameters
5: for client C C do
6:
Optimize g in Eq. with client dataset Dj and g'
7:
Calculate distribution parameters client data with g as in Eq.
8:
Generate features u, with g and perform likelihood estimation with above parameters Optimize as in Eq. with client dataset, generated features, exact likelihood PDę (ū.) and h'
9:
10:
end for
11:
the Server aggregates the parameters of hig and s/ from clients C and weighted averages
the parameters by client data number
12:
end for
13:
end for
14:
Output: the learned classification model h.
Average Forgetting The metric of average forgetting assesses the extend of backward transfer during continual learning, quantified as the disparity between the peak accuracy and the ending accuracy of each task. We also use a weighted average when calculating average forgetting:
Average Forgetting
max te
(a
dx Tú
nng
(11)
Σx
EE
m k=
C.3 OPTIMIZATION
The Adam optimizer is employed for training all models. For all experiments except for CIFARI00. learning rate of le-4 is utilized, with global communication round of 60, and local iteration of 100. We set learning rate as le-3, global communication round as 40, and local iteration as 400 for CIFAR100. Consistent with prior research YYoon et al. 2021a Qi et al. 2023). all clients participate in each communication round. For training. a mini batch size of 64 is adopted. The number of generated samples in an iteration aligns with this mini-bated size. We report the mean and standard deviation each experiment, conducted three times with different random seed.
C.4 MODEL ARCHITECTURES
In the case of CIFAR 100, we utilize the feature extractor of ResNet-18 (He et al. 2016) h, and ha comprises two FC layers both with 512 units. While for other datasets we adopt a three-layer CNN followed by FC layer with 512 units ha The channel numbers of the convolutional layers are [64, 128, 256]. And h& is represented by a FC layer. The outputs ha belong to 5512 All the FC layers employed in the architectures consist of 512 units. The convolutional layers and FC layers are followed by a Leaky ReLU layer. Another FC layer serves as he and operates as the classification head.
The NF models consist of four layers of random permutation layer and affine coupling layer. The random permutation layers randomly permute the input vector that various dependency among dimensions of input vectors could be effectively modeled. The inverse function of random permutation layers is to reversely permute the vector back the original order. The affine coupling layers firstly partition the input vector into two halves I, and Zg. Then an affine transformation is applied to one part of the input, conditioned on the other part:
Ya = exp(s(xa)) @ xs + t(xa).
(12)
y/b Tb,
(13)
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where and t denote functions that create scaling and translation parameters, which implemented with 2 blocks of residual neural network and learned from the data. The output vector y is the concatenation of ya and yb- The invertibility of affine coupling transformation is readily apparent.
D ADDITIONAL EXPERIMENTAL RESULTS
D.1 ABLATION STUDIES
Our method consists of three major components: (I) feature generative-replay (GR). For generative replay, we train global NF model in the feature space of classifier. By augmenting the learning process of classifier with the generated features, we prevent complete forgetting of previous tasks. (II) knowledge distillation (KD). The NF model is trained in the feature space of classifier. To maintain the stability of the training process for the NF model, a knowledge distillation loss is employed in the feature space of classifier, mitigating significant drift. (III) correlation estimation for accurate forgetting (AF). We assess the reliability of the generated feature by its probability density within the current tasks. Leveraging the NF model, we approximate the local feature distribution evaluate the probability of given generated feature aligning with the current distribution.
We conduct ablation studies on the EMNIST-LTP and EMNIST-shuffle dataset as displayed in Table Our method achieves optimal performance with all the three modules. Without the GR module, the module also loses efficacy. Therefore, left with the KD module, the performance of our model comparable to that of PODNet and FLwF2T which relies on knowledge distillation to retain previous knowledge. Without the AF module, our method degrades into naive generative reply based method, thus the performance is close to FedCIL and ACGAN-Replay.
Table 3: Ablation studies on EMNIST-LTP and EMNIST-shufile dataset.
EMNIST-LTP
EMNIST-shuffle
Model
Accuracyt
Forgetting.
Accuracyt
Forgetting
PODNet+FedAvg
36.9
19.8
71.0
3.9
PODNet+FedProx
10.4
14.3
70.6
9.6
ACGAN BEGAN--Repay++FedAAy
38.4+0.2
9.840.8
70.0
4.7
OCGAN-Repla-+FedPMM
41.3
10.4
70.3
6.1
FLwF2T
40.1
15.5
71.0=0.9
8. 8.1
FedCIL
42.0+0.6
12.4.0.3
71.1
6.4+0.2
AF-FCI w/o GR
38.8+1.5
15.3+0.4
70.8
6.740.5
AF FF-FCL w/o KD
44.3+0.6
10.7=0.7
72.1
5.8
AF FCL w/o AF
41.8+0.3
13.7
71.0
6.7
AF FCL
47.5+0.3
7.9
75.8
4.2+0.1
D.2 CIFARIO0 IN A DIFFERENT SETTING
We conduct experiments CIFAR100 with more challenging setting. We randomly sample 10 classes among 100 classes of CIFARI00 as a task for each of the 8 clients, and there are 6 tasks for each client (N T client dataset. Therefore, each client possesses more tasks while less samples per task.
10). For each class, we randomly sample 400 instances into the
As shown Table our method attains the highest accuracy among the evaluated methods. Although the CL methods and conventional FCL methods emphasize the retention of knowledge acquired from previous tasks, indiscriminate memorization of potentially erroneous knowledge can detrimentally impact the performance on previous tasks. In contrast, our proposed method adopts adaptive approach to forgetting biased features, resulting in a notable reduction of forgetting compared to established baselines, thus preserving higher degree of task-specific knowledge retention.
D.3 RESULTS OF EMNIST-NOISY DATASET
We conduct experiments in the EMNIST-noisy dataset with an increasing number noisy clients. We display the complete comparison of accuracy and forgetting among baselines here. It is observed
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Table 4: Average accuracy and forgetting on CIFAR100 when N 8, 7 6, C 10.
Model
Accuracyt
Forgetting
FedAvg
19.3
2.4
FedProx
20.1 10.2
1.9 10.08
PODNet=FedAvg
21.3
2.040.06
PODNet+FedProx
21.6.0.44
2.1
ACGAN- Feea-
19.5
3.040.36
ACGAN-Replay--TedPPo
19.6=0.2
2.8
FLwF2T
21.5
5.940.67
FedCIL
19.610.3
2.9.0.52
GLFC
19.9
3.2
AF-FCL
23.8
0.9
that the performance the methods consistently diminishes with the escalating count of noisy clients, as depicted in Table 5 The presence of noisy clients introduce harmful information into the model learning process and memorization of such information proves detrimental to the overall performance. Thus, some of the CL and FCL methods, which aim to fight forgetting, exhibit inferior performance compared to FL methods. Our approach employs adaptive mechanisms to mitigate the impact of erroneous information. By alleviating the negative influence of noisy clients, method consistently surpasses all baselines in both accuracy and resistance to forgetting.
Table 5: Average accuracy and forgetting on EMNIST-noisy dataset in the last 3 tasks with different number malicious clients M.
M =
M 2
M 4
Model
Accuracyt
Forgetting
Accuracyt
Forgetting.
Accuracyt
Forgetting,
FedAvg
52.3
16.1
51.7
16.0.0.4
50.4
12.1
FedProx
52.5
12.540.4
51.8
18.8
51.040.5
13.5
PODNet+FedAvg
43.3
20.3
38.5
20.1
33.8
19.040.9
PODNet-FedProx
44.3
19.640.7
37.3=1.3
21.2
34.1+1.3
18.440.6
ACGAN-Res CeAANN-Relay+FFdAAy
45.8
18.640.5
42.6=0.9
17.5
40.2
16.040.9
ACGAN-Replan FedPa
50.240.4
18.540.2
43.7=1.0
17.2
39.6
16.4
FLwF2T
52.1
14.742.3
47.6=0.3
18.6g1.9
44.54..5
14.140.3
FedCIL
49.840.4
15.240.9
45.820.7
19.1
42.0
15.841.4
AF AF-ECL
55.5
7.5
54.9
11.8
54.0
12.8
D.4 RESULTS OF IMAGENET-SURSET DATASET
We conducted experiments on subset of the ImageNet dataset. Each client among 10 clients contains 4 tasks, where each task consists of 40 classes among 200 classes. As shown in the table below, our method surpasses existing baselines. This empirical evidence demonstrates the efficacy of our method, particularly in handling richer semantic information on large datasets such ImageNet.
Table 6: Average accuracy and forgetting on ImageNet-Subset dataset when N 10, 4, C 40.
Model
Accuracyt
Forgetting.
FedAvg
14.7
3.2
FedProx
15.1
2.3
ACGAN-Replay+Fen
17.4
1.6
17.3
1.8
FedCIL
17.8
1.2
GLFC
18.0
1.9
AF-FCL
20.4
1.7
17

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Published as a conference paper at ICLR 2024
E COMPUTATION ANALYSIS AND DEVICES
As a generative-replay based model, AF-FCL has a similar number of parameters with other generative-replay based methods, including the baselines FedCIL, ACpaN- etc. Due to the special design of NF models, the generation and density estimation of them are fast and efficient. Therefore, AF-FCL does not bring many extra computational and communication costs. We provide the running-time comparisons with baselines in Table 7 As shown in the table, running-time of the proposed method less than that of the generative-replay based models mentioned above.
Devices In the experiments, we conduct all methods on a local Linux server that has two physical CPU chips (Intel(R) Xeon(R) CPU B5-2640 v4 @ 2.40GHz) and 32 logical kernels. All methods are implemented using Pytorch framework and all models are trained on GeForce RTX 2080 Ti GPUs.
Table 7: Run-time consumption comparisons on the EMNIST-LIP and CIFARIO0 dataset
Run-time consumption (EMNIST-LTP)
Run-time consumption
Methods
(CIFARI00)
FedAvg
22 min
238 min
FedProx
min
245 min
PODNet+FedAvg
35 min
252 min
PODNet+FedProx
37 min
253 min
ACGAN- Replay+
85 min
312 min
ACGAN- ACGAA-Replay+FeddPrx
89 min
315 min
FLwF2T
33 min
248 min
FedCIL
93 min
322 min
AF-FCL
min
302 min
F RELATED NOTIONS
F.1 BIASED FEATURES
Researchers have employed various definitions for biased features, one of which involves defining them as spurious correlations. We denote X' Y' as an input and output space of machine learning algorithm. An algorithm learns mapping from the data x E X' to the prediction ý E V: 1 = f(x). We assume there are attributes 71, 72, abstracted from the data æ. For example, 71 represents the shape of the object in the input image ær and 7/2 denotes the number of black pixels in the input image The machine learning algorithm actually relies many attributes to conduct infering: i = f(7,, "i....., ). We define an attribute y as biased feature if it does not comply with the natural meaning of the target y Jeon al. (2022). Relying such biased attribute would result in poor generalizability of the algorithm. The biased features could be attained through biased training dataset and the learned mapping / relying on the biased features may not perform well in the testing dataset. For instance, if in the training image dataset all cows are standing on the grass, the machine learning model may rely on the attribute 'grass' for classifying images of cows.
In Sec. 4. we instantiate biased features with label noise (Zhang et al. 2021 Chen et al. 2020). With random labels, the model probably extracts misaligned attributes. In benehmark datasets, machine learning models may also learn biased features even without label noise (Zhu et al. 2021a
F.2 CONCEPT DRIFTS
Different from the studies about Federated Continual Learning, the evaluation in the concept drift studies is conducted at each time step. Therefore, there is no memorization requirement or catastrophic forgetting problem in the concept drift studies. A novel clustering algorithms for reacting to concept drifts is proposed (Jothimurugesan al. (023). Adaptive-FedAAC adapted the learning rate to react to concept drift (Canonaco et al. 20011. Panchal et al. proposed to detect concept drift through the magnitude of parameter updates and designed novel adaptive optimizer Panchal al. 2003)
18

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Published as a conference paper at ICLR 2024
F.3 ORTHOGONAL TRAINING
The incorporation of orthogonal training and our accurate forgetting method is a promising direction. Bakman et al. proposed to modify the subspace of model layers learning new tasks such that it is orthogonal to the global principal subspace old tasks (Bakman et al., 2023). By distinguishing the subspace inside the model for each task, catastrophic forgetting of old tasks mitigated, and also relieves the influence of unrelated tasks. We will continue to explore the employment of orthogonal training our method.
Our method explicitly quantifies the correlations of generated features through probability calculations. Moreover, we facilitate selective forgetting by assigning lower weights erroneous old knowledge, thus enabling the classifier to discard biased features and achieve improved overall performance.
19


Reviews:
[{"review_id": "1YDMgZqseU", "reviewer": "Reviewer_pq5J", "summary": "The authors consider the problem of Federated Continual Learning (FCL) in which each client is faced with a sequence of (potentially unrelated) learning tasks. The challenge to be solved is that clients maintain performance across all tasks seen so far while learning in a collaborative fashion s.t. they learn a global model capable of handling all tasks seen by all clients during training.\nA novel method is proposed to tackle a rather general definition of FCL leveraging generative replay with Normalizing Flows (NF), Knowledge Distillation to control feature distribution shift and accurate forgetting which aims to exclude uninformative/harmful samples/features from learning. Experiments were conducted on multiple datasets to show the effectiveness of the proposed method.", "questions": "# Section 5\n- Assume a task $T_{t-1}$ has been learned successfully and now a client faces task $T_t$ which is unrelated to $T_{t-1}$ but $T_{t-1}$ is not contradictory to $T_t$, i.e. does not harm learning. Since the tasks are unrealted it is likely that they are far off each other in the latent space of the NF, hence the proposed method would weight down $T_{t-1}$ while learning $T_t$. Forgetting in this case would be harmful in this case. Have you considered such cases in the experiments?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- the FCL definition used here allows for a broad set of continual learning problems in FL scenarios, including scenarios with unrelated or even contradictory tasks (termed Limtless Task Pool (LTP))\n- the problem is relevant, interesting and well motivated (especially in Sec. 4)\n- the paper is written clearly in general. The problem definition as well as the methodogical contribution is clearly elaborated on\n- it is straightforward to follow through the paper\n- experiments show the effectiveness of the approach and an ablation study gives evidence that the proposed method works well in the presented setting\n- code is available", "weaknesses": "# Section 4\nWhile the problem shown in Sec. 4 sufficiently supports the parts of the motivation of the paper, it lacks to clearly support the claim that biases in the data have severe impact on the model performance in FCL. However, related work already indicated that this is the case, making an explicit empirical validation somewhat obsolete. Nevertheless it would be great to have some references to fully cover the claims in this section as well.\n\n# Section 5\n- \"Besides, learning in feature space avoids the danger of leaking raw data through generative model, thus protecting data privacy\". This does not protect privacy if the model is leaked because one could still run e.g. membership inference attacks given the first feature extractor. Without any additional protection mechanism (e.g. differential privacy) privacy guarantees are not given. Please rephrase that part. [1][2]\n\n- \"The invertability ensures a lossless memory of the original input.\" Yes, but it's more the bijective property that allows for a lossless memory since if the transformations were not bijective, one could not reconstruct the output uniquely. Please consider rephrasing.\n\n- regarding Correlation Estimation: does a large distance of encodings in the latent space of the NF  (i.e. low likelihood)  imply that tasks/samples/features are contradictory? I think it does not, although your proposed way to measure this might have a certain level of accuracy since the distance and the extent of being contradictory probably correlate. It would be beneficial to see how strong the correlation between distance of tasks in the latent space and \"harmfulness\" during learning is.\n\n# Section 6\n- although the experiments cover most of the claims in the paper, there are still doubts to which extent the empirical results support the claims: All experiments consider image classification tasks of either the same dataset (split into several tasks) or very related datasets (e.g. MNIST and SVHN). Although in the LTP (using EMNIST) setting tasks are sampled randomly by each client, there still might be a quite high correlation among tasks as images of different letters still may share certain features (e.g. \"b\" and \"p\"). An additional experiment on a more diverse datasaet such as CIFAR-100 in the LTP setting would be beneficial to see.\n\n# Minor points\nPlease be consistent in the notation:\n- in the CL (Sec.3) definition $\\mathcal{T}^i$ and $D^i$ both refer to datasets.\n- when introducing CL and FL in Sec. 3, in the CL definition the indexes change: While in the CL definition $x^t_i$ refers to the $i$-th sample of task $t$, $x^i_k$ refers to the $i$-the sample on the $k$-th client in the FL definition.\n- Eq. 1: the last loss should be $\\mathcal{L}(\\theta^t, \\mathcal{T}^T)$\n\n# References\n[1] Suri et. al. 2022. Subject Membership Inference Attacks in Federated Learning.\n\n[2] Hatamizadeh et. al. 2022. Do Gradient Inversion Attacks Make Federated Learning Unsafe?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "VhTxp2QEvN", "reviewer": "Reviewer_cH3G", "summary": "This paper presents Accurate Forgetting (AF) for Federated Continual Learning (FCL), a method that effectively leverages past knowledge in federated networks. It tackles the issue of biased or irrelevant features in FCL due to statistical heterogeneity. AF weights the generated data based on the current data distribution, effectively \"forgetting\" data that is heterogeneous to the current client and task. It also memorizes previous data by generating pseudo feature vectors based on the distribution in the latent space, thereby retaining information from previous data.", "questions": "1. In section 5.3, the author claims that \"learning in feature space avoids the danger of leaking raw data through the generative model, thus protecting data privacy\". However, a client could theoretically generate raw data by first using the NF model from the previous FedAvg to generate numerous features, then converting these back to the data space using deconvolution methods with h_a from the last FedAvg.\nEven assuming deconvolution is difficult, AF-FCL still permits each client to generate a feature space distribution, which includes information from all clients in the previous round. This potential problem is not found in standard FL methods.\nIs there a more effective solution to this data privacy concern?\n\n2. The potential for malicious data to emerge during the Federated task is a concern. The proposed AF-FCL appears to \"accurately forget\" prior correct data, an issue not expected with other FCL methods that aim at memorizing everything. The effects are unclear in the following scenarios:\n*    The malicious (falsely labeled) data occurs in the middle of the FL task.\n*    Increased heterogeneity among clients and tasks during the FL task, and comparison of AF-FCL to the baseline.\n\n3. Please provide a more plausible real-world scenario where various classification tasks need to be trained within the same model under Federated Learning (FL). This would illustrate the necessity for \"accurate forgetting\" to manage high levels of heterogeneity. Figure 1 provides a clear example. However, if each hospital has different classification tasks (thorax, brain, liver) that they wish to train together under FL, wouldn't it be more reasonable to conduct them in three separate FL environments?", "limitations": "", "rating": 5, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Rather than preventing forgetting in continual learning settings, the authors introduce an interesting concept that forgetting is crucial even in these settings. They propose a method that accurately forgets heterogeneous or malign information by assigning lower weights to certain generated feature vectors.\n2. They employ a normalizing flow model to retain previous knowledge through distribution in feature space.\n3. The results, supported by ablation studies, highlight the effectiveness and superiority of their proposed accurate forgetting method over existing leading-edge methods.", "weaknesses": "1. In Section 4.2's experiment, noise interference is limited to the initial three tasks. This doesn't adequately assess the impact on each method when the noise occurs at random or during intermediate stages, which would be a more general scenario.\n\n2. The problems in Section 4.2 appear to overlap with those discussed in studies on Concept Drifts in Federated Learning (FL). To emphasize the importance and influence of this research, it would be beneficial to distinguish it from Concept Drifts in Federated Learning, incorporate these into the related works, or conduct additional experiments. Consider citing works such as FEDDRIFT (Jothimurugesan et al., 2022), ADAPTIVEFEDAVG (Canonaco et al., 2021), and Flash (Panchal et al., 2023).\n\n3. AF-FCL allows each client to create a feature space distribution, incorporating information from all clients from the previous round. This potential problem is not found in standard FL methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "i0ApVIuUV2", "reviewer": "Reviewer_BJsd", "summary": "This work suggested the harm of remembering biased or irrelevant features, which could happen in federated continue learning (FCL) scenarios, and designed a generative methods to mitigate erroneous information by correlation estimation with an NF model. The authors have conducted sufficient experiments to validate the effectiveness of the proposed method.", "questions": "See weaknesses.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 2, "contribution": 3, "strengths": "1. The methodology is solid, successfully excluding biased features from the memory bank.\n\n2. The experiments are sufficient.", "weaknesses": "1. The problem formulation is confusing. In section 3.1 and most parts of section 3.2, the authors explain federated continual learning and a limitless task pool in detail, which is irrelevant to the methodology section. If I understand correctly, the main contribution of this work is disentangling and removing biased harmful features, while FCL merely serving as a relevant scenario. It would be better if the authors introduced the FCL formulation briefly and focused on the biased features in the memory bank.\n\n2. It would be better if the authors could illustrate or formally define \"biased features.\"\n\n3. Despite EMNIST-noisy, the authors haven't explained the reason why using all other datasets could introduce biased features.\n\n4. The term 'task' in the paper seems to refer to the 'data domain', instead of the commonly used task definitions (e.g., classification, segmentation, edge estimation), which is confusing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "UpGJh0htpI", "reviewer": "Reviewer_T35E", "summary": "In this paper, the authors discuss the problem of federated continual learning (FCL). Their solution forgetting problem in FCL is to train an NF Model (a type of generative model). This generative model has two benefits: first, it helps the clients to calculate the distribution parameters of the client data; second, it generates features that will be exploited to train the global model. The authors also propose a new task transition called limitless task pool (LTP), which is more suitable for federated settings. In LTP, clients' tasks are independently selected, and they may or may not share any data.", "questions": "1- Do task transitions for the clients happen simultaneously, or do tasks change in different clients independently?\n\n2- Is any example saved in the memory for AF-FCL? What is the memory size for the memory-based baselines (GFCL and FLwF2T)? \n\n3- How robust is AF-FCL to data heterogeneity? Could you please include the information regarding how heterogeneous clients' data distribution is?\n\n4- What is the communication cost for AF-FCL?\n\n5- What is the number of parameters in NF models?\n\n6- Are all the clients participating in the training every round?\n\n7- How is g initialized? What is the forgetting in the NF model?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper is well-motivated. \n* The solution is novel but complex.\n* The paper considers realistic scenarios, and it can outperform the prior works.\n* Paper includes various datasets, prior works, comprehensive ablation on the different components of the loss function, and computation analysis.", "weaknesses": "* It is not straightforward to understand how difficult the baselines are because FedAvg and FedProx, without any continual learning mitigation mechanism, have about 8% forgetting. Maybe this is due to the fact that the performance of the models is not good even in the current tasks.\n* In all the experiments, N (number of clients) is very small (only 10). \n* Please also check out the questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "O5Tux5sFsj", "reviewer": "Reviewer_hHgp", "summary": "The paper presents an interesting and novel approach to addressing the challenges of federated continual learning (FCL), particularly in scenarios where data and tasks among different clients are potentially unrelated or even antagonistic. The concept of \"accurate forgetting\" (AF) and the proposed AF-FCL method are well-motivated and empirically evaluated. The paper is well-written and addresses an important problem in the intersection of federated learning and continual learning.", "questions": "Please see the weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper introduces the novel concept of \"accurate forgetting\" (AF) in the context of FCL. It is commendable to challenge the conventional wisdom that forgetting is invariably detrimental and instead show that in specific situations, it can be beneficial.\n\n2. The paper conducts comprehensive experiments to evaluate the proposed method against various baselines. The results show the superiority of the AF-FCL method, which strengthens the paper's contributions.\n\n3. The paper is well-structured and clearly written.", "weaknesses": "1. The authors' use of feature correlation for replay training is intriguing, though the batch-wise weighting method employed seems somewhat simplistic. Recent research has explored orthogonal training techniques [1] to mitigate the influence of uncorrelated or biased old features. Consequently, a more nuanced feature weighting mechanism might yield improved results.\n\n2. It would be beneficial for the authors to provide a more detailed rationale for the application of the Normalization Flow (NF) model in this context. While any generative model could potentially be used to generate old features with a suitable feature extractor, it is unclear why the NF model was chosen specifically. It may be valuable to conduct experiments to demonstrate the necessity of the NF model in comparison to other generative models.\n\n3. Additional information regarding the architecture of the NF model and the communication cost it incurs would enhance the clarity and practicality of the proposed method.\n\n4. To better simulate the non-iid setting in Federated Learning, it is suggested that the authors consider employing a Dirichlet distribution [2], which could be more representative of real-world data distribution patterns than the current setting.\n\n5. While the paper addresses \"challenging\" datasets, such as CIFAR100 and MNIST-SVHN-F, it is recommended that the authors conduct experiments on truly challenging datasets like ImageNet. Additionally, the paper should include experiments with shuffled task orders for CIFAR100 and other challenging datasets to provide a more comprehensive evaluation of the proposed method.\n\n6. It is advisable for the authors to include comparisons with more recent baseline methods, such as TARGET (ICCV 2023 [3]), to ensure that the proposed approach is benchmarked against the latest state-of-the-art techniques in the field.\n\n[1] Bakman, Yavuz Faruk, et al. \"Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning.\" arXiv preprint arXiv:2309.01289 (2023).\n\n[2] Hsu, Tzu-Ming Harry, Hang Qi, and Matthew Brown. \"Measuring the effects of non-identical data distribution for federated visual classification.\" arXiv preprint arXiv:1909.06335 (2019).\n\n[3] Zhang, Jie, et al. \"TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)