# Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications

arXiv: https://arxiv.org/abs/2310.05269 · HTML: https://mlsanity.com/paper/2310.05269/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 64 of its 267 references that are in the map.
- First posted on arXiv 8 Oct 2023.

## Builds on (most cited first, 30 of 64)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence](https://mlsanity.com/paper/1909.00560/) (2019)
- [Local Differential Privacy based Federated Learning for Internet of Things](https://mlsanity.com/paper/2004.08856/) (2020)
- [Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging](https://mlsanity.com/paper/1909.05125/) (2019)
- [Fast-Convergent Federated Learning with Adaptive Weighting](https://mlsanity.com/paper/2012.00661/) (2020)
- [Eluding Secure Aggregation in Federated Learning via Model Inconsistency](https://mlsanity.com/paper/2111.07380/) (2021)
- [FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring](https://mlsanity.com/paper/2012.07450/) (2020)
- [Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework](https://mlsanity.com/paper/2002.10671/) (2020)
- [Communication-Efficient Adaptive Federated Learning](https://mlsanity.com/paper/2205.02719/) (2022)
- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [Faster Non-Convex Federated Learning via Global and Local Momentum](https://mlsanity.com/paper/2012.04061/) (2020)
- [Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design](https://mlsanity.com/paper/2103.14272/) (2021)
- [DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning](https://mlsanity.com/paper/2111.00465/) (2021)
- [Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning](https://mlsanity.com/paper/2106.03328/) (2021)
- [Dynamic Fusion based Federated Learning for COVID-19 Detection](https://mlsanity.com/paper/2009.10401/) (2020)
- [Joint Privacy Enhancement and Quantization in Federated Learning](https://mlsanity.com/paper/2208.10888/) (2022)
- [When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network](https://mlsanity.com/paper/2009.10601/) (2020)
- [Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach](https://mlsanity.com/paper/2004.03877/) (2020)
- [Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors](https://mlsanity.com/paper/2112.06872/) (2021)
- [Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT](https://mlsanity.com/paper/2202.01512/) (2022)
- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [Joint Service Pricing and Cooperative Relay Communication for Federated Learning](https://mlsanity.com/paper/1811.12082/) (2018)
- [FedHAP: Fast Federated Learning for LEO Constellations Using Collaborative HAPs](https://mlsanity.com/paper/2205.07216/) (2022)
- [Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2005.11901/) (2020)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing](https://mlsanity.com/paper/2110.07136/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments](https://mlsanity.com/paper/2502.10419/) (2025)
- [PUL-Inter-slice Defender: An Anomaly Detection Solution for Distributed Slice Mobility Attacks](https://mlsanity.com/paper/2510.02236/) (2025)
- [Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference](https://mlsanity.com/paper/2607.20124/) (2026)

## Abstract (arXiv)

Robust machine learning (ML) models can be developed by leveraging large volumes of data and distributing the computational tasks across numerous devices or servers. Federated learning (FL) is a technique in the realm of ML that facilitates this goal by utilizing cloud infrastructure to enable collaborative model training among a network of decentralized devices. Beyond distributing the computational load, FL targets the resolution of privacy issues and the reduction of communication costs simultaneously. To protect user privacy, FL requires users to send model updates rather than transmitting large quantities of raw and potentially confidential data. Specifically, individuals train ML models locally using their own data and then upload the results in the form of weights and gradients to the cloud for aggregation into the global model. This strategy is also advantageous in environments with limited bandwidth or high communication costs, as it prevents the transmission of large data volumes. With the increasing volume of data and rising privacy concerns, alongside the emergence of large-scale ML models like Large Language Models (LLMs), FL presents itself as a timely and relevant solution. It is therefore essential to review current FL algorithms to guide future research that meets the rapidly evolving ML demands. This survey provides a comprehensive analysis and comparison of the most recent FL algorithms, evaluating them on various fronts including mathematical frameworks, privacy protection, resource allocation, and applications. Beyond summarizing existing FL methods, this survey identifies potential gaps, open areas, and future challenges based on the performance reports and algorithms used in recent studies. This survey enables researchers to readily identify existing limitations in the FL field for further exploration.