The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Robust Aggregation for Federated Learning
Open accessAbstract
We present a novel approach to federated learning that endows its aggregation process with greater robustness to potential poisoning of local data or model parameters of participating devices. The proposed approach, Robust Federated Aggregation (RFA), relies on the aggregation of updates using the geometric median, which can be computed efficiently using a Weiszfeld-type algorithm. RFA is agnostic to the level of corruption and aggregates model updates without revealing each device’s individual contribution. We establish the convergence of the robust federated learning algorithm for the stochastic learning of additive models with least squares. We also offer two variants of RFA: a faster one with one-step robust aggregation, and another one with on-device personalization. We present experimental results with additive models and deep networks for three tasks in computer vision and natural language processing. The experiments show that RFA is competitive with the classical aggregation when the level of corruption is low, while demonstrating greater robustness under high corruption.
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Self-citationCalibrating Noise to Sensitivity in Private Data Analysis
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Cited by
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772
Self-citationPrivacy and Robustness in Federated Learning: Attacks and Defenses
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Attack of the tails: Yes, you really can backdoor federated learning
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Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
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Metadata
- Authors
- Pillutla, K., Kakade, S. M., & Harchaoui, Z.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2022
- Volume
- 70
- Pages
- 1142-1154
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2996769125
- Cited by
- 801
- References
- 77
- FWCI
- 44.91
- Type
- journal article
- Language
- en
- Open access
- green
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- Pillutla, K., Kakade, S. M., & Harchaoui, Z. (2022). Robust Aggregation for Federated Learning. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2022.3153135
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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