FLOSS: Federated Learning with Opt-Out and Straggler Support

Dahlia J Felten; David J Goetze; Jeannie R Albrecht; Rohit Bhattacharya

arxiv: 2507.23115 · v2 · submitted 2025-07-30 · 💻 cs.LG · cs.AI

FLOSS: Federated Learning with Opt-Out and Straggler Support

David J Goetze , Dahlia J Felten , Jeannie R Albrecht , Rohit Bhattacharya This is my paper

Pith reviewed 2026-05-19 01:55 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords federated learningopt-outstragglersdata privacymissing databias mitigationheterogeneous devices

0 comments

The pith

FLOSS mitigates bias in federated learning caused by users opting out of data sharing and straggling devices.

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

The paper presents FLOSS, a system for federated learning that deals with missing data from two sources: users who choose to opt out of sharing their data and devices that straggle due to varying capabilities. Earlier work on privacy in federated learning assumed all participating users share their data, but current privacy rules allow selective opt-outs, leading to biased datasets when combined with stragglers. FLOSS aims to counteract the resulting bias and performance drop, with simulation results showing improved outcomes compared to standard methods.

Core claim

FLOSS mitigates the impacts of such missing data on federated learning in the presence of stragglers and user opt-out, and empirically demonstrates its performance in simulations.

What carries the argument

FLOSS, a federated learning system designed to handle user opt-outs and device stragglers to reduce bias from incomplete training data.

Load-bearing premise

The simulations used to test FLOSS accurately reflect the bias and performance effects that arise from real user opt-outs and device stragglers in deployed federated learning systems.

What would settle it

Conducting a real-world experiment with actual users opting out at varying rates and measuring whether the model bias and accuracy match the simulation predictions for FLOSS versus standard federated learning.

read the original abstract

Previous work on data privacy in federated learning systems focuses on privacy-preserving operations for data from users who have agreed to share their data for training. However, modern data privacy agreements also empower users to use the system while opting out of sharing their data as desired. When combined with stragglers that arise from heterogeneous device capabilities, the result is missing data from a variety of sources that introduces bias and degrades model performance. In this paper, we present FLOSS, a system that mitigates the impacts of such missing data on federated learning in the presence of stragglers and user opt-out, and empirically demonstrate its performance in simulations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

FLOSS flags a real practical gap in federated learning around opt-outs plus stragglers but the abstract supplies no method or simulation details, so the claim is impossible to assess yet.

read the letter

The paper points out that federated learning has to cope with users who opt out of sharing data and with stragglers from varying device speeds, both creating missing data that hurts model quality. FLOSS is their system to reduce those effects, shown in simulations. What it does well is calling attention to how privacy agreements now include opt-out options, something earlier privacy-focused FL papers did not fully account for alongside hardware heterogeneity. That combination is a fair practical concern. The soft spots are in the execution details, which are absent from the abstract. No method description, no simulation parameters, no baselines or error bars. It's impossible to tell from what's here whether FLOSS actually mitigates bias or if the simulations are set up in a way that makes the problem easier than real deployments. The stress test note is right that the weakest link is how well the simulations reflect real user opt-outs and straggler patterns, including any correlations with the data itself. Without that, the results are hard to interpret. This is for researchers focused on making federated learning work in messy, real-world conditions rather than idealized settings. Readers dealing with voluntary participation and diverse hardware would see the relevance. I would recommend peer review. The idea targets a genuine deployment barrier, and a full paper with clear algorithms and careful experiments could be worth the time even if revisions are needed.

Referee Report

1 major / 1 minor

Summary. The paper introduces FLOSS, a federated learning system that mitigates the impacts of missing data arising from user opt-outs and device stragglers, and empirically demonstrates its performance in simulations.

Significance. If the empirical results hold under realistic modeling of opt-out and straggler dynamics, the work could have practical significance for deploying federated learning in settings with heterogeneous devices and privacy-driven data missingness, potentially reducing bias and improving robustness.

major comments (1)

Abstract: The central claim that FLOSS 'mitigates the impacts of such missing data' and 'empirically demonstrate its performance in simulations' is load-bearing for the contribution, yet the abstract supplies no algorithm details, no description of how opt-out and straggler missingness are generated or correlated with data distributions, no evaluation metrics, no baselines, and no error analysis. This absence prevents any assessment of whether the simulations support the claim or address the fidelity concern for real-world bias effects.

minor comments (1)

Abstract: The acronym FLOSS is used in the title and text without expansion or explanation of its meaning.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the major comment point by point below.

read point-by-point responses

Referee: Abstract: The central claim that FLOSS 'mitigates the impacts of such missing data' and 'empirically demonstrate its performance in simulations' is load-bearing for the contribution, yet the abstract supplies no algorithm details, no description of how opt-out and straggler missingness are generated or correlated with data distributions, no evaluation metrics, no baselines, and no error analysis. This absence prevents any assessment of whether the simulations support the claim or address the fidelity concern for real-world bias effects.

Authors: We agree that the provided abstract is concise and omits the specific details noted, which limits initial assessment of the empirical claims. In the revised manuscript we will expand the abstract to include high-level descriptions of the FLOSS algorithm, the simulation setup for generating opt-out and straggler missingness (including any modeled correlations with data distributions), the primary evaluation metrics, the baselines used, and a summary of the error analysis performed. These additions will be kept brief to preserve abstract length while enabling readers to better evaluate the support for our claims. The full technical details, simulation methodologies, and results are contained in the body of the paper. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected; empirical simulation study with no derivation chain.

full rationale

The abstract presents FLOSS as a system that mitigates missing data effects from opt-outs and stragglers in federated learning, with performance shown via simulations. No equations, algorithms, fitted parameters, self-citations, or derivation steps are provided in the available text. The central claim is an empirical demonstration rather than a mathematical reduction, so no load-bearing step reduces to its own inputs by construction. This qualifies as a self-contained empirical paper with no circularity under the defined criteria.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the contribution is described at the level of a named system and simulation results.

pith-pipeline@v0.9.0 · 5613 in / 1021 out tokens · 48208 ms · 2026-05-19T01:55:23.971361+00:00 · methodology

discussion (0)

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We leverage modern theory in inverse probability weighting (IPW) and missing data graphical models to reweight the gradient aggregation

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