FedEraser: Enabling Efficient Client-Level Data Removal from Federated Learning Models
Abstract
Federated learning (FL) has recently emerged as a promising distributed machine learning (ML) paradigm. Practical needs of the "right to be forgotten" and countering data poisoning attacks call for efficient techniques that can remove, or unlearn, specific training data from the trained FL model. Existing unlearning techniques in the context of ML, however, are no longer in effect for FL, mainly due to the inherent distinction in the way how FL and ML learn from data. Therefore, how to enable efficient data removal from FL models remains largely under-explored. In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning method-ology that can eliminate the influence of a federated client’s data on the global FL model while significantly reducing the time used for constructing the unlearned FL model. The basic idea of FedEraser is to trade the central server’s storage for unlearned model’s construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL. A novel calibration method is further developed to calibrate the retained updates, which are further used to promptly construct the unlearned model, yielding a significant speed-up to the reconstruction of the unlearned model while maintaining the model efficacy. Experiments on four realistic datasets demonstrate the effectiveness of FedEraser, with an expected speed-up of 4× compared with retraining from the scratch. We envision our work as an early step in FL towards compliance with legal and ethical criteria in a fair and transparent manner.
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Liu et al. [54] specified two evaluation parameters: attack precision and attack recall, where attack precision denotes the proportion of unlearned samples that is expected to participate in the training process.
“Two evaluation parameters are specified: attack precision and attack recall , where attack precision denotes the proportion of unlearned samples that is expected to participate in the training process.”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
In FedEraser, in calibration training and update calibration steps, several rounds of a calibration retraining process are performed to approximate the unlearning updates without the target client.
“In calibration training and update calibration steps, several rounds of a calibration retraining process are performed to approximate the unlearning updates without the target client.”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
Liu et al. [54] provided a prediction difference metric, which measures the difference in prediction probabilities between the original global model and the unlearned model.
“In addition, a prediction difference metric is also provided, which measures the difference in prediction probabilities between the original global model and the unlearned model.”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
FedEraser also requires caching of intermediate data, which will cost more storage.
“Second, this solution also requires caching of intermediate data, which will cost more storage.”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
In FedEraser, in the calibrated update aggregating and the unlearned model updating steps, standard federated learning aggregation operations are used to aggregate those unlearning updates and further update the global model.
“In the calibrated update aggregating and the unlearned model updating steps, standard federated learning aggregation operations are used to aggregate those unlearning updates and further update the global model.”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
Liu et al. [54] present an experimental verification method based on a membership inference attack.
“Liu et al. [54] present an experimental verification method based on a membership inference attack.”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
Liu et al. [54] proposed an efficient federated unlearning methodology, FedEraser.
“Based on the idea of trading the central server's storage for the unlearned model's construction, Liu et al. [54] proposed an efficient federated unlearning methodology, FedEraser .”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
In FedEraser, historical parameter updates from the clients are stored in the central server during the training process, and then the unlearning process unfolds in four steps: (1) calibration training, (2) update calibrating, (3) calibrated update aggregating, and (4) unlearned model updating, to achieve the unlearning purpose.
“Historical parameter updates from the clients are stored in the central server during the training process, and then the unlearning process unfolds in four steps: (1) calibration training , (2) update calibrating , (3) calibrated update aggregating , and (4) unlearned model updating , to achieve the unlearning purpose.”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
FedEraser eliminates the influence of the target data.
“This eliminates the influence of the target data.”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
The effectiveness of FedEraser will decrease dramatically as the number of unlearning requests increases; this is because the gradients are cached during the training phase, and the unlearning process will not update these gradients to satisfy subsequent unlearning requests.
“However, the effectiveness of this scheme will decrease dramatically as the number of unlearning requests increases; this is because the gradients are cached during the training phase, and the unlearning process will not update these gradients to satisfy subsequent unlearning requests [54].”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
Attack recall denotes the fraction of unlearned samples that can be correctly inferred as part of the training dataset.
“Attack recall denotes the fraction of unlearned samples that can be correctly inferred as part of the training dataset.”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
They are mostly designed to leverage the special characteristics of a particular learning process or training scheme.
“They are mostly designed to leverage the special characteristics of a particular learning process or training scheme [24, 47, 54].”
From Machine Unlearning: A Survey (2024) · cites this paper as [54]
FedEraser's calibration process relies on historical updates of participating clients, and limited hardware storage makes it hard to deploy in practice.
“The calibration process during the unlearned model training relies on the historical updates of the participating clients. The limited store capability of the hardware makes it hard to deploy in practice.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [224]
FedEraser stores historical updates, reconstructs an unlearned model in the central server based on stored historical parameters once the global model converges, and uses benign clients as calibrating clients to renew the global model to an unlearned WFL global model.
“In [224], the federated unlearning methodology, namely, FedEraser, is proposed. In this scheme, the central server stores the historical updates received from clients during the global model training process. Once the global model converges, the FedEraser reconstructs an unlearned model in the central server based on the stored historical parameters.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [224]
FedEraser requires no substantial modification to the existing WFL architecture and participant training process, so it is easy to implement.
“In the FedEraser design, no substantial modification is required to the existing WFL architecture and the training process of the participants. Thus, the proposed method is easy to implement.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [224]
FedEraser must target the attacker client, which is hard in actual implementation.
“although the training data and the training process are not required, the FedEraser must target the attacker client, which is also pretty hard in actual implementation.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [224]
Defense methods [218], [224] require the historical information of local model updates and are not compatible with SecAgg.
“However, such defense methods [218], [224] require the historical information of local model updates and are not compatible with SecAgg, which brings more difficulty in the practical implementation.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [224]
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F2UL: Fairness-Aware Federated Unlearning for Data Trading
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Self-citationGoldfish: An Efficient Federated Unlearning Framework
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FedWiper: Federated Unlearning via Universal Adapter
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Self-citationResponsible and Effective Federated Learning in Financial Services: A Comprehensive Survey
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Strategic Data Revocation in Federated Unlearning
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Hier-FUN: Hierarchical Federated Learning and Unlearning in Heterogeneous Edge Computing
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Addressing unreliable local models in federated learning through unlearning
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Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging
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DT-FU: Digital Twin-Driven Federated Unlearning for Resilient Vehicular Networks in the 6G Era
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A Systematic Literature Review of Machine Unlearning Techniques in Neural Networks
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UaaS-SFL: Unlearning as a Service for Safeguarding Federated Learning
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Exploring Federated Unlearning: Review, Comparison, and Insights
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A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions
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Client-Free Federated Unlearning via Training Reconstruction with Anchor Subspace Calibration
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The Price of Unlearning: Identifying Unlearning Risk in Edge Computing
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Decentralized Unlearning for Trustworthy AI-Generated Content (AIGC) Services
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Speed up Federated Unlearning With Temporary Local Models
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Federated Unlearning and Server Right to Forget: Handling Unreliable Client Contributions
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How to Forget Clients in Federated Online Learning to Rank?
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Don't Forget Too Much: Towards Machine Unlearning on Feature Level
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Appro-Fun: Approximate Machine Unlearning in Federated Setting
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FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks
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SoK : Federated Learning and Unlearning for Medical Image Analysis
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Adaptive Clipping and Distillation Enabled Federated Unlearning
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Machine Unlearning Through Fine-Grained Model Parameters Perturbation
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Machine Unlearning Methodology Based on Stochastic Teacher Network
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Defending Against Data and Model Backdoor Attacks in Federated Learning
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FedSweep: Unlearning Backdoors in Federated Learning
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Self-citationFederated Learning for Medical Image Analysis: Privacy-Preserving Paradigms and Clinical Challenges
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Self-citationMalicious Clients and Contribution Co-Aware Federated Unlearning
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Incentivized Federated Learning and Unlearning
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Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs
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Label Inference Attacks Against Federated Unlearning
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Pseudo unlearning via sample swapping with hash
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FU-PA: Federated Unlearning via Parameters Adjustment
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Self-citation
- Authors
- Liu, G., Ma, X., Yang, Y., Wang, C., & Liu, J.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Hubei University (CN)
- Simon Fraser University (CA)
- Venue
- 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)
- Publisher
- IEEE
- Published
- 2021-06-25
- Pages
- 1-10
- ISSN
- 1548-615X (ISSN-L)
- OpenAlex
- W3197363474
- Cited by
- 248
- References
- 38
- Citations by year
- 2026: 66 · 2025: 94 · 2024: 62 · 2023: 23 · 2022: 3
- FWCI
- 10.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Workshop on Quality of Service (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
- Natural Science Foundation of Hubei Province
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- federated learning, data deletion, right to be forgotten, data poisoning, model reconstruction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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