A Survey on Federated Learning in Human Sensing

Ga\v{s}per Slapni\v{c}ar; Marc Langheinrich; Martin Gjoreski; Mitja Lu\v{s}trek; Mohan Li; Nicholas D. Lane; Pietro Barbiero

REVIEW 4 major objections 6 minor 2 cited by

A Survey on Federated Learning in Human Sensing

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A systematic review of 211 federated-learning studies in human sensing finds that most papers ignore the field's hardest real-world problems, and only a minority demonstrate the reliability needed for practical deployment.

desk verdict A useful, transparent survey of FL in human sensing whose quantitative gap analysis needs a coding reliability check before the specific rankings are cited. read the letter →

arxiv 2501.04000 v1 pith:ZCTZXJBO submitted 2025-01-07 cs.LG cs.HC

Mohan Li , Martin Gjoreski , Pietro Barbiero , Gašper Slapničar , Mitja Luštrek , Nicholas D. Lane , Marc Langheinrich This is my paper · ORCID

classification cs.LGcs.HC

keywords federatedlearninghumansensingsurveytaxonomyeight-dimensionalassessmentprivacysystemheterogeneityactivityrecognition

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

Federated learning promises to train machine-learning models on privacy-sensitive human-sensing data—activity, speech, physiological signals, location—without moving raw data off users' devices. This survey asks how far that promise has actually been kept. It assembles a corpus of 211 studies across six application domains and scores each against eight challenge dimensions (privacy and security, communication cost, system versus statistical heterogeneity, use of unlabeled data, simplified setups, and server- versus client-oriented optimization). The core finding is that the field has concentrated on the easiest-to-simulate problems—statistical heterogeneity and server-side model quality—while privacy defenses, heterogeneous device participation, and learning from unlabeled data remain comparatively neglected, so that only a minority of studies meet the reliability bar for real deployment. If the survey's map is right, it gives practitioners a prioritized list of where to put effort next.

What carries the argument

The carrying object is the eight-dimensional assessment: each surveyed paper is coded with a binary checkmark for whether it addresses privacy and security, communication cost, system heterogeneity, statistical heterogeneity, unlabeled data usage, simplified setup, server-optimized FL, or client-optimized FL. The dimension definitions operationalize each challenge (e.g., privacy counts only if the paper adds defenses such as differential privacy or secure aggregation or runs a vulnerability analysis; system heterogeneity counts only if training or deployment involves heterogeneous devices). Layered on top of a taxonomy of six application domains and nine raw-data types, this coding lets the survey turn a heterogeneous literature into comparable percentages and a ranking of research gaps.

What would settle it

Re-code the same 211 papers with an independent coder (or with the authors plus a second coder) using a written coding manual; if the per-dimension consideration rates differ materially for privacy/security or system heterogeneity, the survey's ranking of research gaps is not reproducible. Alternatively, compile all FL-human-sensing papers published before November 2023 that the search missed and check whether they predominantly address system heterogeneity.

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Extended reading notes

Core claim

The paper's central claim is that, assessed across eight dimensions, current federated-learning research in human sensing is lopsided: statistical heterogeneity and server-optimized federated learning are the characteristics analyzed most frequently, whereas privacy and security, communication cost, system heterogeneity, and unlabeled-data usage receive far less attention. Across the six application domains—audio and speech processing, well-being, user identification, human mobility and localization, activity recognition, and interface development—activity recognition and well-being dominate the corpus. The survey concludes that only a minority of the reviewed studies achieve the reliability necessary for deployment in practical settings, and it identifies five aspects needing urgent research: privacy and security under active attacks, unlimited participation across heterogeneous devices, exploiting unlabeled data in the wild, clarifying whether the primary target is the server or the clients, and moving beyond simplified experimental setups.

Load-bearing premise

The percentages and gap rankings rest on the assumption that the search and exclusion criteria produced a representative corpus of FL-in-human-sensing papers and that the binary checkmarks in the eight-dimensional assessment were applied consistently, and the paper does not report an inter-rater reliability check or a detailed coding manual.

Editorial extensions

If this is right

Practitioners selecting an FL approach for a human-sensing product should expect that most published baselines have not hardened privacy against inference or poisoning attacks, so production systems need additional defenses.
“Unlimited participation” is the paper's label for a concrete open problem: most studies assume homogeneous, well-provisioned clients, so stragglers and low-end devices are implicitly excluded; the survey argues this must change for worldwide deployment.
Because the corpus is weighted toward activity recognition and well-being, the five urgent research directions apply with different force per domain; interface development, with the fewest studies, is the least explored.
The simplified-setup dimension is treated as an undesired feature, and its prevalence implies reported accuracies may overstate real-world performance.
Researchers should treat the paper's priority list as a portfolio recommendation: privacy under attacks, system heterogeneity, unlabeled data, and server/client target clarity.

Reading between the lines

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

The survey's own evidence suggests its five priority directions could be re-derived as a coverage gap: the three dimensions with the lowest checkmark rates (system heterogeneity, privacy defenses, unlabeled-data usage) map almost one-to-one onto the first three urgent research aspects; one testable extension is to recompute the dimension-by-dimension percentages after a re-coding with a formal cod
A second extension: a prospective author could use the eight dimensions as a submission checklist, turning the survey into a de facto evaluation rubric for new FL-human-sensing papers.
If the claim that only a minority are deployment-ready is taken literally, a natural next study is to define an explicit deployment-readiness threshold (e.g., a minimum number of satisfied dimensions plus a real-device evaluation) and measure what fraction of the 211 papers pass; the survey does not itself formalize that threshold.

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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

This is a genuinely useful survey, and the main caveat is isolated to one methodological step. It fills a real gap: prior FL surveys cover IoT, healthcare, and recommender systems, but not human sensing as defined here. The corpus of 211 papers, the raw-data taxonomy, and the six-domain organization are new organizational tools. The PRISMA-style search is described in detail, with explicit venues and exclusion rules; that is more transparent than most surveys in this space.

The authors are honest about the state of the field. They explicitly note that most studies use simplified setups and that only a minority approach deployment-level reliability. The discussion of open challenges is sensible, and the five research directions follow from the analysis rather than being bolted on.

The soft spot is the eight-dimensional assessment. The binary checkmarks in Tables 2–9 are the basis for the quantitative claims about which FL characteristics are most and least analyzed. There is no coding manual, no inter-rater reliability score, and no statement of how N/A versus no-check entries are treated when computing the Figure 5 percentages. The 'Server-optimized FL' dimension is particularly fragile: every FL deployment has a server aggregation step, so without an explicit rule to distinguish deliberate server-side optimization from default FedAvg, those checkmarks could be over-assigned. 'Statistical Heterogeneity' carries a similar risk if the mere presence of non-IID data is coded as addressing the challenge rather than as an explicit robustness analysis.

That weakens the specific ranking—that Statistical Heterogeneity and Server-optimized FL are the most frequently analyzed characteristics. The qualitative pattern, though, is consistent with what I know of the field: privacy and system heterogeneity are under-addressed, while non-IID data gets routine attention. So the overall conclusion is plausible; the issue is a validation gap, not evidence of carelessness.

Who is this for: anyone working on FL for wearable, audio, video, or location-based sensing, plus researchers looking for open problems in this intersection. It deserves peer review. A referee should ask for coding reliability or a sensitivity analysis, but that is a revision request, not a rejection.

I would send it to review and likely accept after minor revisions. I would also cite it if I were writing in this area.

Referee Report

4 major / 6 minor

Summary. The paper presents a systematic survey of federated learning (FL) in human sensing, following the PRISMA methodology. It constructs a corpus of 211 papers from three digital libraries and thirteen selected conferences, organizes them into a taxonomy of six application domains (audio/speech processing, well-being, user identification, human mobility and localization, activity recognition, and interface development), and evaluates each study against eight dimensions: privacy and security, communication cost, system heterogeneity, statistical heterogeneity, unlabeled data usage, simplified setup, server-optimized FL, and client-optimized FL. Based on the aggregated eight-dimensional assessment, the paper claims that Statistical Heterogeneity and Server-optimized FL are the most frequently analyzed characteristics, that system heterogeneity and privacy are under-addressed, and it proposes five research areas requiring urgent attention.

Significance. If the corpus and the eight-dimensional codings are reliable, this survey is a valuable contribution: it is the first PRISMA-based systematic mapping of FL in human sensing, provides a reusable taxonomy and per-domain tables (Tables 2–9) that are a useful reference resource, and makes concrete, falsifiable claims about research gaps. The search strategy and exclusion rules are described transparently, which is a notable strength for a survey. However, the central quantitative claims about which FL characteristics are most or least analyzed rest entirely on binary checkmarks that are not validated, and the paper provides no inter-rater reliability, no coding manual, and no sensitivity analysis. The ranking of research gaps and the related recommendations are therefore not yet supported by the evidence presented.

major comments (4)

[Section 2 (dimensions 4 and 7); Tables 2–9; Figure 5] The binary checkmarks for Statistical Heterogeneity and Server-optimized FL are assigned using definitions that do not reliably distinguish explicit consideration from incidental features of the setup. For Statistical Heterogeneity, the text states both that a system 'addresses' the challenge if it 'applies strategies to ensure robustness against non-IID-related attacks' and that the authors 'expect systems using datasets that are inherently heterogeneous to explicitly analyze the impact of such heterogeneity on FL performance.' These criteria can yield different codings for the same paper. For Server-optimized FL, the definition is that 'the server employs methods to enhance model convergence speed and overall performance,' which is satisfied by virtually any FL deployment, including vanilla FedAvg. Without an explicit coding rule (for example, requiring a server-side algorithm beyond default aggregation or an ablation demonstrating the server-side contribution), the checkmarks are easily over-assigned. Since the paper's headline conclusion that these two dimensions are the 'most analyzed' is computed from these checkmarks, the conclusion is not yet supported. I recommend adding a detailed coding manual, reporting inter-rater reliability on a random subset (e.g., Cohen's kappa), and conducting a sensitivity analysis in which the ambiguous dimensions are re-coded under stricter criteria to demonstrate that the ranking in Figure 5 is robust.
[Section 10, Figure 5] The treatment of N/A entries in the percentage computations is unspecified. The tables contain N/A in many rows (e.g., Table 2, row [293]; Table 3, row [67]; Table 5, row [223]), and the histograms in Figure 5 show three categories: Consideration, Not Applicable, and No Consideration. The paper does not state whether the denominator for each dimension excludes N/A entries or treats them as 'No Consideration.' If N/A entries are excluded, the percentages for different dimensions are not directly comparable because the denominators differ; if they are included as 'No Consideration,' the percentage is distorted. Please specify the exact formula used and justify the treatment; this is essential for interpreting the central claim about which dimensions are most frequently analyzed.
[Section 3.3, Exclusion for Irrelevance] The exclusion criteria for irrelevance are described only in prose, with no operational definitions or reliability check. The categories 'non-application papers' and 'application papers in other fields' require subjective judgments, particularly the boundary between human sensing and adjacent areas such as IoT and medical research. The manuscript does not report how many papers were excluded under each criterion, nor whether screening was performed independently by multiple reviewers. Because the final corpus of 211 papers is the evidence base for all meta-conclusions, the reproducibility of the screening should be documented—for example, with a PRISMA flow diagram that includes exact counts per exclusion reason and a dual-screening protocol with disagreement resolution, or at least a sensitivity analysis with alternative inclusion rules.
[Section 11, Conclusion] The statement that 'only a minority achieve the reliability necessary for deployment in practical settings' is not derived from the eight-dimensional assessment in a transparent way. No composite metric or threshold is defined that maps the binary checkmarks onto a reliability judgment, and the paper does not say how the eight dimensions are combined (e.g., whether all eight must be satisfied, or a subset). Please specify how the dimensions are combined to determine deployment readiness, or soften the conclusion to match what the presented data can support.

minor comments (6)

[Section 3.1, reference list] Reference [38], cited for the ACM Computing Classification System, is listed as 'Generate Code. 1998'; this appears to be an artifact, and it should be corrected to the proper ACM CCS citation.
[Table 3, row for [36]] The application label 'S,FER' is unclear; it should be written as 'SER/FER' or 'Multi' to match the study's use of both speech and facial inputs.
[Section 3.1, Figure 3] The PRISMA flow diagram is referenced but the actual numbers at each stage (identification, screening, exclusion, inclusion) are not visible in the manuscript text; the final version should include the full flow diagram with counts per exclusion reason to support the reproducibility claim.
[Section 10.2] The statement that 'system heterogeneity is the least addressed attribute in the corpus' is presented without an explicit comparison to other under-addressed dimensions such as privacy and unlabeled data usage; please clarify whether this refers to the overall corpus and show the underlying percentages from Figure 5.
[Section 2, dimension 4] The discussion of statistical heterogeneity would benefit from distinguishing data skewness in features, labels, and temporal distributions, since the coding rule currently conflates these forms of non-IID data.
[Throughout] There are several minor typographical errors, including 'collaoborative' in Section 7.2 and 'ZHuang' in reference [312]; these should be corrected in a final pass.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the survey's corpus-level findings are external judgments over 211 papers, not reductions of its own definitions.

full rationale

This paper is a systematic literature survey, not a derivation with fitted parameters or predicted quantities. Its central claims—the corpus composition, the eight-dimensional assessment of each surveyed study, and the resulting rankings of how often each FL characteristic is addressed—are external judgments about other papers. The eight dimensions are stipulative definitions introduced by the authors, and applying them to each surveyed paper is an empirical coding task; the conclusions do not reduce to the definitions themselves. For example, the claim that Statistical Heterogeneity and Server-optimized FL are the most frequently analyzed characteristics is computed from binary checkmarks in Tables 2–9, which are judgments about external papers rather than consequences of the paper's own assumptions. Similarly, the claim that only a minority of studies achieve reliability for deployment is an aggregate of those external codings. The survey does contain self-citations (e.g., references [60], [70], [71], [81], [120], and [219] include the authors), but these are simply included among the 211 surveyed papers and are not load-bearing: no central conclusion is justified solely by the authors' own prior results, and there is no imported uniqueness theorem or ansatz. Concerns about inter-rater reliability, coding consistency, or subjective exclusion criteria are validity risks rather than circularity: an unreliable measurement is not the same as a conclusion that is true by construction. No equation, fitted parameter, or cited result is shown to be equivalent to the paper's own inputs, so no circular step can be exhibited.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The survey has no fitted parameters or invented entities. Its central claims rest on two domain assumptions: that the corpus is representative, and that the eight-dimensional coding is reliable. Both are reasonable but unvalidated, so the correctness risk is medium.

assumptions (2)

domain assumption The three digital libraries plus the selected conferences cover the relevant literature on federated learning in human sensing.
Section 3.1 justifies the search strategy but does not validate its completeness; missing venues or non-indexed papers could alter the corpus.
domain assumption The binary eight-dimensional assessment is a valid way to measure whether a paper addresses a challenge.
Section 2 defines the dimensions, but the paper does not validate the coding scheme or show that independent coders would agree.

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Cite this review

Pith. "Pith review of A Survey on Federated Learning in Human Sensing." pith.science (2026). https://pith.science/paper/ZCTZXJBO

@misc{pith2026250104000,
  author       = {Pith},
  title        = {Pith review of: A Survey on Federated Learning in Human Sensing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZCTZXJBO}},
  note         = {Machine review of arXiv:2501.04000}
}

read the original abstract

Human Sensing, a field that leverages technology to monitor human activities, psycho-physiological states, and interactions with the environment, enhances our understanding of human behavior and drives the development of advanced services that improve overall quality of life. However, its reliance on detailed and often privacy-sensitive data as the basis for its machine learning (ML) models raises significant legal and ethical concerns. The recently proposed ML approach of Federated Learning (FL) promises to alleviate many of these concerns, as it is able to create accurate ML models without sending raw user data to a central server. While FL has demonstrated its usefulness across a variety of areas, such as text prediction and cyber security, its benefits in Human Sensing are under-explored, given the particular challenges in this domain. This survey conducts a comprehensive analysis of the current state-of-the-art studies on FL in Human Sensing, and proposes a taxonomy and an eight-dimensional assessment for FL approaches. Through the eight-dimensional assessment, we then evaluate whether the surveyed studies consider a specific FL-in-Human-Sensing challenge or not. Finally, based on the overall analysis, we discuss open challenges and highlight five research aspects related to FL in Human Sensing that require urgent research attention. Our work provides a comprehensive corpus of FL studies and aims to assist FL practitioners in developing and evaluating solutions that effectively address the real-world complexities of Human Sensing.

Figures

Figures reproduced from arXiv: 2501.04000 by the authors.

**Figure 2.** Our proposed eight-dimensional assessment of a typical FL framework [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** An Overall Workflow of PRISMA in This Survey [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: An overview of the proposed taxonomy in this survey. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

**Figure 5.** Figure 5: A summary pie chart. For each section, a histogram illustrates the percentage of studies that covered a specific FL [PITH_FULL_IMAGE:figures/full_fig_p027_5.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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Gradient-based label leakage attacks recover activity class labels from federated HAR updates with high accuracy, especially under sequential sampling, and standard local privacy defenses provide only partial protection.
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FedAli adds label-free local and global prototype layers, matched by optimal transport, to personalized federated learning, improving cross-client generalization most clearly on the HHAR activity recognition benchmark.

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