Federated Continual Learning: Concepts, Challenges, and Solutions

Ehsan Hallaji; Parisa Hamedi; Roozbeh Razavi-Far

REVIEW 3 major objections 4 minor 2 cited by

Federated Continual Learning: Concepts, Challenges, and Solutions

T0 review · 3 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read This survey maps federated continual learning's challenges to their roots and shows that transformer models, unlike convolutional ones, stay effective when updates are asynchronous.

desk verdict Useful but rough FCL survey; the sync-vs-async experiment is confounded and should not support the architecture-dependent claim. read the letter →

arxiv 2502.07059 v2 pith:7TFRBM6Q submitted 2025-02-10 cs.LG cs.AI

Parisa Hamedi , Roozbeh Razavi-Far , Ehsan Hallaji This is my paper · ORCID

classification cs.LGcs.AI

keywords FederatedcontinuallearningIncrementalNon-stationarydataConceptdriftCatastrophicforgettingAsynchronousaggregation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey argues that federated continual learning (FCL) is best understood as the intersection of federated learning and continual learning, and that its main difficulties—global and local catastrophic forgetting, heterogeneity, communication overhead, and privacy loss—arise from the interaction of distributed training with non-stationary data. It organizes those difficulties into a taxonomy of global, local, and knowledge-transfer challenges and reviews the solution families used against each. The paper also contributes a small comparison on CIFAR-100 with five class-incremental tasks, finding that synchronous aggregation outperforms asynchronous aggregation with a ResNet-18 backbone, while Vision Transformer based methods stay competitive under asynchronous updating. If the finding holds, it matters because real deployments often cannot synchronize all clients, and it makes the backbone architecture a decisive factor in choosing between update mechanisms.

What carries the argument

The central organizing device is the survey's fishbone taxonomy of FCL challenges (Figure 6), which separates global model challenges (forgetting, overfitting, negative knowledge transfer, and accuracy loss), client model challenges (local forgetting), and knowledge-dissemination issues (updating frequency and client participation). It does the work of linking each challenge to root causes in federated learning and continual learning and of organizing the solution families reviewed in Tables 2–4. The supporting experimental machinery is the CIFAR-100 comparison of synchronous and asynchronous aggregation across ResNet-18 and Vision Transformer backbones, which carries the paper's architecture-dependent conclusion.

What would settle it

Re-run the comparison on a second benchmark, such as a 10-task split of CIFAR-10 or a domain-incremental stream, with multiple random seeds and fixed communication budgets, and check whether ResNet-18 still shows a clear synchronous-over-asynchronous gap while ViT methods do not; if the gap disappears or reverses, the paper's architecture-dependent conclusion would be refuted.

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Extended reading notes

Core claim

The paper's central claim is that federated continual learning is not just federated learning applied to a moving target: the interaction of distributed collaboration and non-stationary data creates distinct failure modes that must be categorized by whether they appear globally, locally, or during knowledge transfer. On the basis of the surveyed literature and its own CIFAR-100 experiment, the paper argues that synchronous aggregation generally leads to more reliable performance and better retention of learned knowledge, particularly with convolutional architectures like ResNet-18, while ViT-based models demonstrate that asynchronous aggregation can still perform competitively, making it a practical alternative in scenarios where full client synchronization is difficult to achieve.

Load-bearing premise

The central experimental claim rests on the assumption that a single CIFAR-100 comparison, split into five class-incremental tasks under one Dirichlet partitioning and reported without variance, fairly represents how synchronous versus asynchronous updating behaves across real federated continual-learning systems.

Editorial extensions

If this is right

With convolutional backbones such as ResNet-18, practitioners should prefer synchronous aggregation because the paper's CIFAR-100 comparison shows it gives higher global accuracy and lower forgetting.
With transformer backbones such as ViT, asynchronous aggregation is a practical alternative when full client synchronization is hard, since the paper reports competitive accuracy with minimal forgetting.
The choice of updating mechanism cannot be evaluated in isolation: the paper's results indicate that the backbone architecture changes which strategy wins, so evaluations should report both.
The paper's taxonomy implies that FCL solutions need to combine FL-style handling of non-IID data with CL-style replay, distillation, or regularization at both global and local levels.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the architecture-dependence is real, the benefit of asynchronous updating should grow with the degree of task overlap and the expressiveness of the encoder; varying the Dirichlet parameter and the number of clients in a re-run would make that trend visible.
The paper's taxonomy points toward an untested combination: privacy mechanisms that spend noise budget over time (temporal differential privacy) interacting with replay or distillation buffers, since both consume memory and distort model updates.
The reported comparison lacks multiple seeds, code, and hyperparameter details, so its main claim should be read as evidence for a hypothesis rather than a settled ranking; an open-source re-implementation with fixed compute budgets would test it cleanly.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The survey is a genuinely useful entry point to federated continual learning. It organizes a scattered literature into sensible taxonomies, and the large tables mapping methods to challenges are the kind of thing I would point a new student toward. The coverage of recent work is reasonably current, and the discussion of local versus global forgetting is a fair framing. It does not break new conceptual ground, but it does a credible job of consolidating what exists.

The soft spots are real, and one is load-bearing. Section 5.2 claims that synchronous aggregation is more reliable with ResNet-18 while ViT models stay competitive under asynchrony. That conclusion is not supported by the experimental design. The synchronous and asynchronous conditions differ not just in update timing but also in the data partition: the sync setting uses a shared task sequence with varying class proportions, while the async setting gives clients partially overlapping and private classes. Those are different problems. On top of that, the two architecture groups are entangled with different method families: ResNet-18 is paired with FedAvg, FedProx, FedEWC, GLFC, while ViT is paired with prompt-based methods like FedViT and FedL2P. So the ResNet drop could come from harder label heterogeneity or from the method family, not from synchronous versus asynchronous aggregation per se. The paper's interpretation ('delayed or uncoordinated client updates') is an explanation, not something the protocol isolates. Given that this is the paper's most concrete new claim, that is a significant problem.

The experimental presentation is also thin: single runs, no error bars, no significance testing, no code, no hyperparameters. The reader's weakest-assumption point is fair. The editing issues are minor by comparison but still real: duplicated sentences in 5.1.1, a garbled paragraph in Section 6, and Table 4 listing FedStream and FedET twice with conflicting rows. Those are fixable, but they reduce confidence in the survey's reliability.

The survey itself is honest and cites the relevant literature; the self-citation to the authors' prior survey is not a problem here. The taxonomy is derivative, but that is what surveys often are. As a reference document, it has value.

Who is this for? A graduate student or researcher looking for a map of FCL challenges and methods. It deserves a serious referee because the survey content is useful, but the experimental section should either be substantially reworked to control for data partition and method family, or removed and replaced with a clearly labeled discussion of existing comparisons. I would not cite it as a definitive source on sync versus async until that is fixed.

Referee Report

3 major / 4 minor

Summary. The manuscript is a survey of Federated Continual Learning (FCL) that organizes the field into FL-side challenges (heterogeneity, resource constraints, communication overhead, model stability, privacy) and CL-side challenges (concept drift, catastrophic forgetting, stability-plasticity dilemmas), and then proposes a taxonomy of FCL-specific global and local challenges with corresponding solution families (knowledge distillation, exemplar replay, gradient manipulation, regularization). It includes extensive tables summarizing methods, datasets, and challenge coverage, plus a short original experimental comparison in Section 5.2 contrasting synchronous and asynchronous aggregation on CIFAR-100 with ResNet-18 and ViT backbones. The paper's main new claim is that synchronous aggregation is more reliable for convolutional architectures like ResNet-18, while ViT-based models tolerate asynchronous updates competitively.

Significance. If the architecture-dependent sync-versus-async claim were rigorously established, it would offer a practical design guideline for FCL systems. The survey also provides a broad reference: Tables 1-7 consolidate recent FCL literature, the dataset tables (Tables 2-3) are useful entry points, and the global/local distinction for forgetting is a helpful organizing device. However, the load-bearing experimental claim in Section 5.2 is currently confounded and under-reported, and there are internal inconsistencies in Table 4 and Table 5 that undermine the reliability of the survey as a reference. The paper does not provide code, multiple seeds, or hyperparameter details, so the empirical conclusions cannot be validated as stated.

major comments (3)

[Section 5.2, paragraph beginning 'The evaluation is conducted on CIFAR-100...'] The synchronous and asynchronous conditions differ in two important ways at once: the data partition (same task sequence with Dirichlet alpha=1 versus partially overlapping class distributions with private classes) and the method family (ResNet-18 paired with fine-tuning methods such as FedAvg and GLFC, versus ViT paired with prompt-based methods such as FedViT and FedDualP). Any observed performance gap could therefore be caused by task heterogeneity or method family rather than by the synchronization mechanism. To support the claim that 'synchronous aggregation generally leads to more reliable performance... particularly with convolutional architectures' while ViT tolerates asynchrony, the comparison must isolate the update mechanism by holding data partition and method family fixed and varying only sync/async.
[Section 5.2, Figure 9] The experimental comparison is not reproducible as reported. Figure 9 shows bar charts with no error bars, the text does not specify the number of clients, local epochs, batch size, learning rate, optimizer, or how many random seeds were used, and no code is released. Single runs without variance or significance testing cannot support a broad architectural conclusion. The manuscript should either add a full experimental protocol with multiple seeds and error bars, or reframe this subsection as a motivating illustration rather than an empirical finding.
[Section 5.1.3, Table 5] Table 5 is internally inconsistent and does not support the claim that 'FedViT consistently outperforms FedKNOW across all tasks.' The column headers ('Accuracy FR Accuracy FR Improvement (%)') do not align with the fourteen numeric entries per row; the task-wise values exceed 100% (e.g., FedViT entries of 103.03-110.66), which is impossible for accuracy or percentage-point improvement, and the forgetting rates (FR) are only reported for the client-count columns, not per task. The reader cannot determine whether the numbers are task accuracies, cumulative improvements, or something else. This table needs to be corrected or removed, and the text revised to match the corrected data.

minor comments (4)

[Section 5.1.1, paragraph on local exemplar] The sentence 'On the other hand, local exemplar refers to each client retaining a subset of data from previous tasks for use in future training.' is repeated verbatim, creating a redundant passage that should be reduced to a single occurrence.
[Section 6, paragraph on labeled data requirements] The paragraph beginning 'The first category relates to situations...' contains a garbled repetition: the complete sentence about requiring sufficient labeled data at the beginning of training appears twice with only slight wording changes. This section should be rewritten to state each conditional scenario once.
[Table 4, rows for FedET and FedStream] FedET is listed twice under reference [97] with conflicting entries (base model 'ResNet/ViT-Base' versus 'ResNet/ViT/Bert-Base-Uncased' and different aggregation descriptions), which will confuse readers. Additionally, the two distinct FedStream papers ([72] and [73]) share a name and similar row formatting; consider adding clarifying annotations such as the year or a distinguishing initial.
[Throughout] The manuscript contains numerous typographical and spacing errors, including 'assocciated', 'procesure', 'alliviate', 'non stationary environment,which', and frequent instances of 'di fferent' with an extra space. A careful proofreading pass is needed.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; the survey's claims rest on cited external literature, and the one self-citation is descriptive, not load-bearing.

full rationale

This is a survey paper, so its load-bearing content is a taxonomy of challenges and a review of existing methods rather than a derivation of new results. I checked each place where the authors appear to reach a conclusion from their own material. Section 5.2 presents an empirical comparison of synchronous versus asynchronous updating on CIFAR-100. The concluding claim that 'synchronous aggregation generally leads to more reliable performance and better retention of learned knowledge, particularly with convolutional architectures like ResNet-18' is interpreted from Figure 9, which plots accuracies of named external methods (FedAvg, FedProx, GLFC, FedViT, FedMGP, etc.). Those accuracies are not fitted to the conclusion, and the conclusion is not encoded in the definitions of the methods. At most, the comparison is confounded: the synchronous and asynchronous conditions differ in data partition (same task sequence with Dirichlet proportions versus partially overlapping private classes) and in method family (ResNet-18 full-fine-tuning methods versus ViT prompt-based methods), so the architecture-dependent reading is not experimentally isolated. That is a validity and reproducibility concern, not circularity. The only self-citation is reference [4], a prior decentralized-FL survey by two of the present authors. It is used descriptively in Table 1 and in the sentence 'Notably, [4, 16] deeply examine various attack vectors and defense mechanisms in FL systems.' No central premise of this paper is justified by that citation, and no uniqueness theorem or derivation is imported from it. The paper does not define any quantity in terms of a target result, does not rename a known empirical pattern as a new derivation, and does not fit parameters and then call them predictions. Therefore the appropriate finding is no significant circularity, with a low score reflecting only the presence of a minor, non-load-bearing self-citation.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

As a survey, the paper has no invented entities. Its central experimental claim relies on experimental choices (alpha, number of tasks, hidden hyperparameters) and on the assumption that the reviewed literature is representative. These assumptions are not proven in the text.

free parameters (3)

Dirichlet alpha = 1
Controls the degree of non-IID data partitioning among clients in the Section 5.2 experiment; the sync vs async conclusion may change with different alpha.
Number of tasks = 5
CIFAR-100 was split into 5 class-incremental tasks; the comparison depends on this task schedule.
Unspecified hyperparameters (learning rate, epochs, batch size) = not reported
The experimental section does not specify these values, and the reported results depend on them.

assumptions (3)

domain assumption The selected surveyed papers are representative of the FCL literature
The survey bases its taxonomy and conclusions on the works it reviews, but the inclusion criteria are not documented.
ad hoc to paper The authors' own experimental setup compares methods fairly
Section 5.2 assumes that the listed baselines were run under comparable conditions, but no code or full configuration is given.
domain assumption Continual learning techniques transfer to federated settings as described
The taxonomies in Figures 2 and 10 map CL solutions to FCL, assuming the mapping is valid.

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Cite this review

Pith. "Pith review of Federated Continual Learning: Concepts, Challenges, and Solutions." pith.science (2026). https://pith.science/paper/7TFRBM6Q

@misc{pith2026250207059,
  author       = {Pith},
  title        = {Pith review of: Federated Continual Learning: Concepts, Challenges, and Solutions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7TFRBM6Q}},
  note         = {Machine review of arXiv:2502.07059}
}

read the original abstract

Federated Continual Learning (FCL) has emerged as a robust solution for collaborative model training in dynamic environments, where data samples are continuously generated and distributed across multiple devices. This survey provides a comprehensive review of FCL, focusing on key challenges such as heterogeneity, model stability, communication overhead, and privacy preservation. We explore various forms of heterogeneity and their impact on model performance. Solutions to non-IID data, resource-constrained platforms, and personalized learning are reviewed in an effort to show the complexities of handling heterogeneous data distributions. Next, we review techniques for ensuring model stability and avoiding catastrophic forgetting, which are critical in non-stationary environments. Privacy-preserving techniques are another aspect of FCL that have been reviewed in this work. This survey has integrated insights from federated learning and continual learning to present strategies for improving the efficacy and scalability of FCL systems, making it applicable to a wide range of real-world scenarios.

Figures

Figures reproduced from arXiv: 2502.07059 by the authors.

**Figure 2.** Taxonomy of approaches used to address global forgetting. While this taxonomy is created based on the literature on CL, it can be applied to FCL, as it [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

**Figure 3.** Task-wise accuracy evolution in CL using EWC on CIFAR-10: The plot illustrates the performance trajectory of a CL model trained using EWC on the [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: Measures commonly used for catastrophic forgetting [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]

**Figure 5.** Figure 5: The Stability–Plasticity Trade-off in Continual Learning: A Dilemma-Centric Perspective overfitting, and negative knowledge transfer. The solutions to these challenges are also explained [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

**Figure 6.** Figure 6: Fishbone diagram showing the taxonomy of challenges in FCL. Main branches show the main categories of these challenges, and sub-branches indicate [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

**Figure 7.** Figure 7: Categorization of techniques used to mitigate negative knowledge [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]

**Figure 8.** Figure 8: Cumulative Accuracy Improvement Comparison Between FedViT [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]

**Figure 9.** Figure 9: Evaluation of the aggregated global model’s accuracy on local [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]

**Figure 10.** Figure 10: Taxonomy of methods used to tackle local forgetting. [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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