FLOSS: Federated Learning with Opt-Out and Straggler Support
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 22 references that are in the map. First posted on arXiv 30 Jul 2025.
Builds on
10 of 22 references.
References · 10
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2016Deep Learning with Differential Privacycited by 2,728
- 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 186
- 2018Graphical Models for Processing Missing Datacited by 58
- 2018Residual Unfairness in Fair Machine Learning from Prejudiced Datacited by 49
- 2020The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspectivecited by 17
- 2024Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacementcited by 8
- 2024Vertical Federated Learning with Missing Features During Training and Inferencecited by 6
- 2023Causal Inference With Outcome-Dependent Missingness And Self-Censoringcited by 5
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Citing papers per year: 2026: 1.
1 paper.