Learn How to Query from Unlabeled Data Streams in Federated Learning

Jun Zhang; Tao Lin; Xinran Li; Yuchang Sun

REVIEW 3 major objections 5 minor 42 references

Learn How to Query from Unlabeled Data Streams in Federated Learning

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read LeaDQ treats federated data querying as a multi-agent decision problem and learns local selection policies that improve global model accuracy beyond current federated active learning baselines.

desk verdict Streaming federated active learning cast as a Dec-POMDP with QMIX; a solid contribution whose evaluation is undermined by an un-costed labeled held-out set at the server. read the letter →

arxiv 2412.08138 v2 pith:MJ555F6X submitted 2024-12-11 cs.LG cs.AIcs.DC

Yuchang Sun , Xinran Li , Tao Lin , Jun Zhang This is my paper · ORCID

classification cs.LGcs.AIcs.DC

keywords federatedlearningactivedatastreamsmulti-agentreinforcementQMIXDec-POMDPnon-IIDquerying

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 paper targets a gap in federated learning: client data arrive as unlabeled streams, labeling is expensive, and the samples that help one client's local model are not necessarily the samples that help the global model. The authors claim that data querying in this setting should be framed as a collaborative decentralized decision problem rather than a local heuristic. They propose LeaDQ, a multi-agent reinforcement learning algorithm that trains each client's local sample-selection policy against a global reward: the improvement in the federated model's accuracy on a held-out set. Experiments on SVHN, CIFAR-100, 20Newsgroup, and Tiny-ImageNet show LeaDQ consistently beating uncertainty sampling, coreset selection, LoGo, and KAFAL. If the claim holds, federated systems can spend their labeling budget on samples that actually advance the global model, not just each client's local view.

What carries the argument

The load-bearing mechanism is the Dec-POMDP/QMIX combination. A Dec-POMDP formalizes the fact that each client chooses which samples to query under partial observation—only its own logits—while the objective is joint. QMIX provides the training-time bridge between local policies and the global goal: the joint Q-value is Qtot = ψ(Q1, ..., QK, sr), a monotonic function of the local Q-values conditioned on the global state sr, and the network is updated by a temporal-difference loss against the shared held-out accuracy reward. This is what lets the algorithm train locally executable policies against a signal no single client can see, without ever moving raw data off the clients.

What would settle it

Run the paper's SVHN setup with a mismatched held-out set (for example, CIFAR-100 in place of SVHN); if the global state and reward lose all information about the target distribution, the proposed method should collapse to baseline-level accuracy if the central claim about learning from global signals is right.

Watch

Extended reading notes

Core claim

The paper's central discovery is that stream-based federated active learning can be cast as a Dec-POMDP in which every client is an agent. Each agent's local observation is the current global model's predictive logits on the newly arrived unlabeled samples, its action is the choice of which Nq samples to label, and all agents share the reward Rr = Acc(θr; Dheld) − Acc(θr−1; Dheld), the change in held-out accuracy after training on the newly queried labels. Training uses the QMIX factorization: a monotonic mixing network combines local Q-values with a global state (the model's maximum prediction confidence on the held-out set) into a joint Q-value, so each client can learn a decentralized policy that is nevertheless optimized for the global objective. The accompanying motivating experiment shows where the benefit comes from: a Global Coreset that coordinates selections across clients clearly beats Local Coreset, and LeaDQ approaches the global oracle's accuracy while keeping data decentralized.

Load-bearing premise

The method assumes the server has a labeled held-out dataset that follows the target distribution, because that dataset is the only source of both the global state signal and the reward used to train the querying policies; without it, there is no learning signal.

Editorial extensions

If this is right

In stream-based federated active learning, a learned query policy can beat both generic active-learning heuristics (uncertainty, coreset) and federated-specific baselines (LoGo, KAFAL) under non-IID client data.
Because execution only requires the current model's logits and the local policy network, the querying decisions stay decentralized and compatible with FedAvg-style privacy constraints.
The method remains effective when the number of clients grows to 50 and when data heterogeneity is varied in both distribution-based and quantity-based ways.
The learned policies select samples whose label and feature distributions resemble the target global distribution rather than the local client distribution, which is the mechanism behind the accuracy gains.
The framework can be adapted to different arrival volumes and query budgets by changing the action constraint |a| = Nq, and its performance is stable across episode-length hyperparameters.

Reading between the lines

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

The method's practical reach depends on the labeled held-out set; a deployment without a representative D_held would need a proxy signal (for example, confidence calibrated on a small validation slice or a self-supervised estimate), and that substitution is the most direct testable extension.
Since the reward is measured after full FedAvg rounds, policy training is expensive; computing the reward on a small validation batch or every few rounds would make the approach more plausible in bandwidth-limited settings.
The QMIX monotonicity constraint limits the interaction structure among clients; richer factorizations could capture cases where one client's querying decision should negatively influence another's, which the current formulation rules out.

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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

LeaDQ is a serious attempt at a real problem: when unlabeled data arrive in streams at FL clients, how to decide which samples to label. The framing as a Dec-POMDP and the use of QMIX to learn local policies with a global reward is new in this setting, and the paper is mostly clear. The experiments are extensive — SVHN, CIFAR-100, 20Newsgroup, Tiny-ImageNet, varying skew and client count — and the code is available. On the face of it, LeaDQ consistently beats uncertainty, coreset, LoGo, and KAFAL. That is worth refereeing.

The largest soft spot is the server's labeled held-out set. The global state (Eq. 8) is the model's confidence on D_held, and the reward (Eq. 10) is the accuracy delta between rounds on that same set. The paper states this in Section 5.1, but never charges the labels of D_held against the annotation budget, and the baselines receive no analogous labeled validation data. So part of the reported gain may be purchased with labels that the problem setting treats as scarce. That is a load-bearing unfairness in the comparison, not a fatal flaw, but it needs to be addressed — either by using an unlabeled proxy for the reward, or by giving the baselines the same budget and reporting the total label cost.

A few secondary issues: results are averaged over three seeds with no error bars or significance tests, so we cannot tell whether gains are real or noise. The reward itself is a one-round accuracy delta on a fixed 1000-sample held-out set, which is noisy; no sensitivity to D_held size or composition is reported. KAFAL collapses under quantity skew (Table 4), which suggests a possible implementation issue — the paper doesn't comment on it. And the claim in the motivating example that LeaDQ 'achieves comparable performance' to Global Coreset is made without any variance information.

The framing and the algorithm are still the main contribution, and the work is clearly on the right track. The citation pattern is fair; the related work covers both federated active learning and RL-based AL. I'd send this to a serious referee, but with a request for a careful look at the held-out-label issue and a statistical comparison. If those are fixed, this would be a credible contribution to the federated active learning subfield.

Referee Report

3 major / 5 minor

Summary. The paper studies active data querying in federated learning when unlabeled samples arrive at clients as a data stream. It formulates the querying process as a Dec-POMDP and proposes LeaDQ, a QMIX-based multi-agent reinforcement learning approach in which each client's local policy selects N_q samples per round from its arrived unlabeled stream, while a server-side mixing network coordinates the local Q-functions using a global state derived from a held-out dataset. The reward is the one-round change in held-out accuracy. Experiments on SVHN, CIFAR-100, 20Newsgroup, and Tiny-ImageNet compare LeaDQ with Uncertainty, Coreset, LoGo, and KAFAL, reporting consistent accuracy improvements.

Significance. If the claims hold, LeaDQ is a useful step toward principled, learned querying policies for streaming federated active learning. The paper provides a clean problem formulation, a reasonable CTDE instantiation via QMIX, a public code release, a motivating experiment, and evaluations across image and text tasks with non-IID data. The central weakness is that the policy-training signal in Eq. (10) requires ground-truth labels for a server-held dataset of size 1000 (Table 6), and this annotation cost is never charged to LeaDQ's budget nor given to the baselines. In addition, the empirical evidence rests on three-seed averages without error bars or significance tests, and one state-of-the-art baseline (KAFAL) collapses in two of the reported settings without explanation. These issues currently prevent the paper from establishing its headline claim of outperforming the benchmarking algorithms under an equal annotation budget.

major comments (3)

[Section 5.1, Eq. (10), Table 6] The reward R^r = Acc(θ^r; D_held) − Acc(θ^{r−1}; D_held) explicitly requires ground-truth labels for the held-out dataset D_held, whose size is listed as 1000 in Table 6. The server uses these labels to train the query policy, while the uncertainty, coreset, LoGo, and KAFAL baselines receive no analogous labeled validation set. Because the paper's motivating problem treats labels as expensive and scarce, the reported accuracy gains may come from the extra labeled data used only by LeaDQ rather than from the learned querying policy itself. Please either remove the label requirement (e.g., use an unlabeled proxy for the reward), add an equal-annotation-budget comparison in which the baselines also receive the same labeled held-out data for their own tuning, or explicitly justify why the D_held labels should not be counted. In addition, no experiment varies the size or composition of D_held, so overfitting of the policy to that particular held-out set is unexplored.
[Appendix B.2, Fig. 4, Tables 2–4] The main results are averaged over three random seeds with no error bars, standard deviations, or significance tests. Several of the reported margins are extremely small or even negative: in Table 3 (α=1.0) LeaDQ is 66.50% versus KAFAL's 66.48%, and in Table 2 (Nq/Nu=2/10) LeaDQ is 76.28% versus KAFAL's 76.32%. Without per-seed results or a statistical comparison, the claim that LeaDQ consistently outperforms the baselines is not supported, especially for the later-round curves in Fig. 4 where the plotted lines appear close.
[Table 4 and Table 7] KAFAL, a state-of-the-art federated active learning baseline, collapses to roughly 20% accuracy in the quantity-skew setting (Table 4) and on Tiny-ImageNet (Table 7), while it performs competitively elsewhere (e.g., 64.67% in Table 3 with α=0.5). The paper does not explain this collapse or provide any diagnostic, such as sensitivity to hyperparameters or a check of whether the implementation is appropriate for these setups. If KAFAL is not properly configured for these scenarios, the comparison is unfair; if it genuinely fails, that is an interesting finding that should be analyzed. Please add an explanation or ablation, or remove the affected rows with justification.

minor comments (5)

[Fig. 4, Section 6.2] There are typos: '20Newsgrpup' in the Fig. 4 subplot label, 'dsitribution' in Section 6.2, and 'Global Corset' in the Fig. 3 caption should be 'Global Coreset'.
[Table 5] The third column header of Table 5 reads 'SVHN' but the column reports the DistilBERT model and should be labeled '20Newsgroup'.
[Appendix B.1] The sentence 'LeCun et al. 1998.' is missing its closing parenthesis; it should be 'LeCun et al. 1998)'.
[Table 5] The row 'Total arrived samples in Table 1' is confusing: Table 1 is the dataset summary, not the implementation-detail table. Please renumber or reword this row so it refers to the correct table.
[Section 5.1] The Dec-POMDP tuple is written as ⟨S, A, P, R, O, K, γ⟩, which omits the observation function and the number of agents in the standard notation. This is a minor formal clarity issue, but the tuple should be defined consistently with the text that follows.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: LeaDQ's RL reward is an optimization signal, and reported test accuracy is measured on external benchmarks.

full rationale

The paper's derivation chain does not reduce to its own inputs. The query policy is trained by multi-agent RL with reward R^r = Acc(theta^r; D_held) - Acc(theta^{r-1}; D_held) (Eq. 10) and global state given by prediction confidence on D_held (Eq. 8), but the claimed outcome is model accuracy on held-out test sets of SVHN, CIFAR-100, 20Newsgroup, and Tiny-ImageNet, which are external to the training reward. This is a standard RL objective, not a fitted parameter renamed as a prediction. The paper compares LeaDQ against Uncertainty, Coreset, LoGo, and KAFAL under the same federated training protocol, so the empirical comparison is self-contained against external benchmarks. The only self-citation (Li et al. 2023, co-authored by T. Lin) appears in a related-work list and is not load-bearing. The server's labeled held-out dataset D_held is used to compute the reward and state without being counted against the annotation budget; this is a fairness and external-validity limitation, not a circularity, because Eq. 10 is not an identity that by construction guarantees the reported test accuracy. Accordingly, no circular step is identified.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical or conceptual entities; its key burdens are the labeled held-out set at the server and the sufficiency of logits as observations. The free parameters are standard MARL hyperparameters.

free parameters (4)

discount factor gamma = 0.99
Chosen from Table 6; affects temporal credit assignment in QMIX.
episode length J = 10
Chosen from Table 6; defines the horizon for discounted returns; ablation shows minimal impact.
warm-up timesteps = 32
Initial rounds use an uncertainty heuristic instead of the learned policy; affects early training.
held-out dataset size = 1000
Size of the labeled set used for state and reward computation; if too small, the reward becomes noisy.

assumptions (4)

domain assumption The server holds a labeled dataset D_held following the target distribution.
Invoked in Section 5.1 for the state (Eq. 8) and reward (Eq. 10); without it, there is no learning signal for the query policies.
domain assumption Aggregated client data distribution equals the target distribution P.
Section 3, assumption that the union of local distributions equals P; needed for the global objective to be meaningful.
domain assumption Predictive logits are a sufficient observation for selecting informative samples.
Section 5.1, Eq. 9; the policy relies solely on logits, ignoring raw features or other statistics.
standard math The QMIX monotonic mixing constraint holds for this problem.
Borrowed from Rashid et al. 2018; ensures the global argmax can be computed from local argmax, but is not re-justified for this setting.

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Cite this review

Pith. "Pith review of Learn How to Query from Unlabeled Data Streams in Federated Learning." pith.science (2026). https://pith.science/paper/MJ555F6X

@misc{pith2026241208138,
  author       = {Pith},
  title        = {Pith review of: Learn How to Query from Unlabeled Data Streams in Federated Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MJ555F6X}},
  note         = {Machine review of arXiv:2412.08138}
}

read the original abstract

Federated learning (FL) enables collaborative learning among decentralized clients while safeguarding the privacy of their local data. Existing studies on FL typically assume offline labeled data available at each client when the training starts. Nevertheless, the training data in practice often arrive at clients in a streaming fashion without ground-truth labels. Given the expensive annotation cost, it is critical to identify a subset of informative samples for labeling on clients. However, selecting samples locally while accommodating the global training objective presents a challenge unique to FL. In this work, we tackle this conundrum by framing the data querying process in FL as a collaborative decentralized decision-making problem and proposing an effective solution named LeaDQ, which leverages multi-agent reinforcement learning algorithms. In particular, under the implicit guidance from global information, LeaDQ effectively learns the local policies for distributed clients and steers them towards selecting samples that can enhance the global model's accuracy. Extensive simulations on image and text tasks show that LeaDQ advances the model performance in various FL scenarios, outperforming the benchmarking algorithms.

Figures

Figures reproduced from arXiv: 2412.08138 by the authors.

**Figure 1.** Workflow of the proposed LeaDQ framework. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** An overview of the proposed LeaDQ framework. The yellow block is the process of active data querying and the blue [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

Figures from the paper (3 more)

**Figure 3.** Figure 3: Comparisons between different data querying [PITH_FULL_IMAGE:figures/full_fig_p004_3.png]

**Figure 4.** Figure 4: Model accuracy (%) in different data querying rounds. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Visualization of the data samples in SVHN dataset [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

, " * write output.state after.block = add.period write newline

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[2] [2]

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[3] [3]

Ahn, J.; Ma, Y.; Park, S.; and You, C. 2024. Federated Active Learning (F-AL): An Efficient Annotation Strategy for Federated Learning. IEEE Access , 12: 39261--39269

work page 2024

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T.; and Adams, R

Ash, J. T.; and Adams, R. P. 2020. On Warm-Starting Neural Network Training. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems (NeurIPS)

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Cacciarelli, D.; and Kulahci, M. 2024. Active learning for data streams: A survey. Mach. Learn., 113(1): 185--239

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Cao, Y.; Shi, Y.; Yu, B.; Wang, J.; and Tao, D. 2023. Knowledge-Aware Federated Active Learning with Non-IID Data. In IEEE/CVF International Conference on Computer Vision (ICCV) , 22222--22232

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