# Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework

arXiv: https://arxiv.org/abs/2401.01493 · HTML: https://mlsanity.com/paper/2401.01493/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 63 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [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)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Data-Free Knowledge Distillation for Heterogeneous Federated Learning](https://mlsanity.com/paper/2105.10056/) (2021)
- [FedKD: Communication Efficient Federated Learning via Knowledge Distillation](https://mlsanity.com/paper/2108.13323/) (2021)
- [FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning](https://mlsanity.com/paper/2009.01974/) (2020)
- [FedALA: Adaptive Local Aggregation for Personalized Federated Learning](https://mlsanity.com/paper/2212.01197/) (2022)
- [Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better](https://mlsanity.com/paper/2112.09824/) (2021)
- [FedFed: Feature Distillation against Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2310.05077/) (2023)
- [Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data](https://mlsanity.com/paper/2301.09152/) (2023)
- [DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2202.00972/) (2022)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Continual Local Training for Better Initialization of Federated Models](https://mlsanity.com/paper/2005.12657/) (2020)
- [LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization](https://mlsanity.com/paper/2206.11743/) (2022)
- [Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration](https://mlsanity.com/paper/2309.11103/) (2023)
- [Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attacks](https://mlsanity.com/paper/2205.11857/) (2022)
- [Federated Prompt Learning for Weather Foundation Models on Devices](https://mlsanity.com/paper/2305.14244/) (2023)
- [ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences](https://mlsanity.com/paper/2207.13897/) (2022)
- [Knowledge Distillation in Vision Transformers: A Critical Review](https://mlsanity.com/paper/2302.02108/) (2023)
- [TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regression](https://mlsanity.com/paper/2304.14131/) (2023)
- [Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery](https://mlsanity.com/paper/2106.00880/) (2021)
- [Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images](https://mlsanity.com/paper/2210.16584/) (2022)
- [Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning](https://mlsanity.com/paper/2306.00792/) (2023)
- [A Privacy Preserving System for Movie Recommendations Using Federated Learning](https://mlsanity.com/paper/2303.04689/) (2023)

## Built on by (most cited first, 6 of 6)

- [FeDaL: Federated Dataset Learning for General Time Series Foundation Models](https://mlsanity.com/paper/2508.04045/) (2025)
- [Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification](https://mlsanity.com/paper/2405.15405/) (2024)
- [Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach](https://mlsanity.com/paper/2604.06727/) (2026)
- [A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification](https://mlsanity.com/paper/2503.10262/) (2025)
- [Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats](https://mlsanity.com/paper/2509.00634/) (2025)
- [Less is more: Ensemble Learning for Retinal Disease Recognition Under Limited Resources](https://mlsanity.com/paper/2402.09747/) (2024)

## Abstract (arXiv)

Remote Sensing Target Fine-grained Classification (TFGC) is of great significance in both military and civilian fields. Due to location differences, growth in data size, and centralized server storage constraints, these data are usually stored under different databases across regions/countries. However, privacy laws and national security concerns constrain researchers from accessing these sensitive remote sensing images for further analysis. Additionally, low-resource remote sensing devices encounter challenges in terms of communication overhead and efficiency when dealing with the ever-increasing data and model scales. To solve the above challenges, this paper proposes a novel Privacy-Reserving TFGC Framework based on Federated Learning, dubbed PRFL. The proposed framework allows each client to learn global and local knowledge to enhance the local representation of private data in environments with extreme statistical heterogeneity (non. Independent and Identically Distributed, IID). Thus, it provides highly customized models to clients with differentiated data distributions. Moreover, the framework minimizes communication overhead and improves efficiency while ensuring satisfactory performance, thereby enhancing robustness and practical applicability under resource-scarce conditions. We demonstrate the effectiveness of the proposed PRFL on the classical TFGC task by leveraging four public datasets.