# Federated Learning: Opportunities and Challenges

arXiv: https://arxiv.org/abs/2101.05428 · HTML: https://mlsanity.com/paper/2101.05428/

## Key facts

- Cited by 84 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 14 Jan 2021.

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Can You Really Backdoor Federated Learning?](https://mlsanity.com/paper/1911.07963/) (2019)
- [One-Shot Federated Learning](https://mlsanity.com/paper/1902.11175/) (2019)
- [Distilled One-Shot Federated Learning](https://mlsanity.com/paper/2009.07999/) (2020)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [A Review of Challenges and Opportunities in Machine Learning for Health](https://mlsanity.com/paper/1806.00388/) (2018)
- [Data Poisoning Attacks on Federated Machine Learning](https://mlsanity.com/paper/2004.10020/) (2020)
- [Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging](https://mlsanity.com/paper/2007.06537/) (2020)
- [Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach](https://mlsanity.com/paper/1905.07479/) (2019)
- [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019)
- [FLaaS: Federated Learning as a Service](https://mlsanity.com/paper/2011.09359/) (2020)
- [Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise](https://mlsanity.com/paper/2007.09208/) (2020)
- [BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture](https://mlsanity.com/paper/2010.07427/) (2020)

## Built on by (most cited first, 30 of 84)

- [Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model](https://mlsanity.com/paper/2305.04063/) (2023)
- [A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions](https://mlsanity.com/paper/2507.18910/) (2025)
- [PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection](https://mlsanity.com/paper/2210.03221/) (2022)
- [FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMs](https://mlsanity.com/paper/2306.04959/) (2023)
- [Industry-Scale Orchestrated Federated Learning for Drug Discovery](https://mlsanity.com/paper/2210.08871/) (2022)
- [Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Training](https://mlsanity.com/paper/2111.01338/) (2021)
- [Tighter Regret Analysis and Optimization of Online Federated Learning](https://mlsanity.com/paper/2205.06491/) (2022)
- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [Federated Learning and Meta Learning: Approaches, Applications, and Directions](https://mlsanity.com/paper/2210.13111/) (2022)
- [Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation](https://mlsanity.com/paper/2204.03500/) (2022)
- [Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection](https://mlsanity.com/paper/2402.05294/) (2024)
- [FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data](https://mlsanity.com/paper/2411.14717/) (2024)
- [Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions](https://mlsanity.com/paper/2110.04160/) (2021)
- [Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI](https://mlsanity.com/paper/2302.11571/) (2023)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [A Theoretical Insight into Attack and Defense of Gradient Leakage in Transformer](https://mlsanity.com/paper/2311.13624/) (2023)
- [An Efficient Privacy-aware Split Learning Framework for Satellite Communications](https://mlsanity.com/paper/2409.08538/) (2024)
- [One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models](https://mlsanity.com/paper/2311.08870/) (2023)
- [Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks](https://mlsanity.com/paper/2311.14469/) (2023)
- [Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gain](https://mlsanity.com/paper/2405.09409/) (2024)
- [Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots](https://mlsanity.com/paper/2410.02783/) (2024)
- [FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization](https://mlsanity.com/paper/2503.12649/) (2025)
- [Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources](https://mlsanity.com/paper/2109.14046/) (2021)
- [Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications](https://mlsanity.com/paper/2207.08486/) (2022)
- [CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models](https://mlsanity.com/paper/2405.13746/) (2024)
- [Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means](https://mlsanity.com/paper/2505.17836/) (2025)
- [On the Efficiency of Privacy Attacks in Federated Learning](https://mlsanity.com/paper/2404.09430/) (2024)
- [I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey](https://mlsanity.com/paper/2404.10386/) (2024)
- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)
- [Multi-PA: A Multi-perspective Benchmark on Privacy Assessment for Large Vision-Language Models](https://mlsanity.com/paper/2412.19496/) (2024)

## Abstract (arXiv)

Federated Learning (FL) is a concept first introduced by Google in 2016, in which multiple devices collaboratively learn a machine learning model without sharing their private data under the supervision of a central server. This offers ample opportunities in critical domains such as healthcare, finance etc, where it is risky to share private user information to other organisations or devices. While FL appears to be a promising Machine Learning (ML) technique to keep the local data private, it is also vulnerable to attacks like other ML models. Given the growing interest in the FL domain, this report discusses the opportunities and challenges in federated learning.