# Federated Learning for Face Recognition with Gradient Correction

arXiv: https://arxiv.org/abs/2112.07246 · HTML: https://mlsanity.com/paper/2112.07246/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion](https://mlsanity.com/paper/1912.08795/) (2019)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Federated Accelerated Stochastic Gradient Descent](https://mlsanity.com/paper/2006.08950/) (2020)
- [The Devil of Face Recognition is in the Noise](https://mlsanity.com/paper/1807.11649/) (2018)
- [Deeply learned face representations are sparse, selective, and robust](https://mlsanity.com/paper/1412.1265/) (2014)
- [Federated Learning with Only Positive Labels](https://mlsanity.com/paper/2004.10342/) (2020)
- [GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition](https://mlsanity.com/paper/2005.10497/) (2020)
- [BroadFace: Looking at Tens of Thousands of People at Once for Face Recognition](https://mlsanity.com/paper/2008.06674/) (2020)
- [FedFace: Collaborative Learning of Face Recognition Model](https://mlsanity.com/paper/2104.03008/) (2021)
- [A 3D GAN for Improved Large-pose Facial Recognition](https://mlsanity.com/paper/2012.10545/) (2020)
- [Federated Face Recognition](https://mlsanity.com/paper/2105.02501/) (2021)

## Built on by (most cited first, 24 of 24)

- [Eliminating Domain Bias for Federated Learning in Representation Space](https://mlsanity.com/paper/2311.14975/) (2023)
- [GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning](https://mlsanity.com/paper/2308.10279/) (2023)
- [PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark](https://mlsanity.com/paper/2312.04992/) (2023)
- [Can Textual Gradient Work in Federated Learning?](https://mlsanity.com/paper/2502.19980/) (2025)
- [FedForgery: Generalized Face Forgery Detection with Residual Federated Learning](https://mlsanity.com/paper/2210.09563/) (2022)
- [Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter](https://mlsanity.com/paper/2305.12449/) (2023)
- [FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning](https://mlsanity.com/paper/2501.05496/) (2025)
- [COALA: A Practical and Vision-Centric Federated Learning Platform](https://mlsanity.com/paper/2407.16560/) (2024)
- [Federated Unsupervised Domain Adaptation for Face Recognition](https://mlsanity.com/paper/2204.04382/) (2022)
- [Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning](https://mlsanity.com/paper/2505.01788/) (2025)
- [Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization](https://mlsanity.com/paper/2309.06692/) (2023)
- [Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learning](https://mlsanity.com/paper/2412.07062/) (2024)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [PAFedFV: Personalized and Asynchronous Federated Learning for Finger Vein Recognition](https://mlsanity.com/paper/2404.13237/) (2024)
- [Personalized Federated Learning Techniques: Empirical Analysis](https://mlsanity.com/paper/2409.06805/) (2024)
- [Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognition](https://mlsanity.com/paper/2501.01816/) (2025)
- [A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems](https://mlsanity.com/paper/2306.16023/) (2023)
- [Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack](https://mlsanity.com/paper/2403.10558/) (2024)
- [AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning](https://mlsanity.com/paper/2405.13467/) (2024)
- [Meta-Learning for Federated Face Recognition in Imbalanced Data Regimes](https://mlsanity.com/paper/2408.16003/) (2024)
- [FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution](https://mlsanity.com/paper/2503.13745/) (2025)
- [Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning](https://mlsanity.com/paper/2509.10635/) (2025)
- [Privacy Preserved Federated Learning with Attention-Based Aggregation for Biometric Recognition](https://mlsanity.com/paper/2510.01113/) (2025)
- [Pattern Recognition Tasks with Personalized Federated Learning](https://mlsanity.com/paper/2605.27816/) (2026)

## Abstract (arXiv)

With increasing appealing to privacy issues in face recognition, federated learning has emerged as one of the most prevalent approaches to study the unconstrained face recognition problem with private decentralized data. However, conventional decentralized federated algorithm sharing whole parameters of networks among clients suffers from privacy leakage in face recognition scene. In this work, we introduce a framework, FedGC, to tackle federated learning for face recognition and guarantees higher privacy. We explore a novel idea of correcting gradients from the perspective of backward propagation and propose a softmax-based regularizer to correct gradients of class embeddings by precisely injecting a cross-client gradient term. Theoretically, we show that FedGC constitutes a valid loss function similar to standard softmax. Extensive experiments have been conducted to validate the superiority of FedGC which can match the performance of conventional centralized methods utilizing full training dataset on several popular benchmark datasets.