# Federated Learning: A Signal Processing Perspective

arXiv: https://arxiv.org/abs/2103.17150 · HTML: https://mlsanity.com/paper/2103.17150/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 73 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 30 of 39)

- [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)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [signSGD: Compressed Optimisation for Non-Convex Problems](https://mlsanity.com/paper/1802.04434/) (2018)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Sparsified SGD with Memory](https://mlsanity.com/paper/1809.07599/) (2018)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding](https://mlsanity.com/paper/1610.02132/) (2016)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [UVeQFed: Universal Vector Quantization for Federated Learning](https://mlsanity.com/paper/2006.03262/) (2020)
- [Generalized Byzantine-tolerant SGD](https://mlsanity.com/paper/1802.10116/) (2018)
- [Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://mlsanity.com/paper/1910.13067/) (2019)
- [Robust Federated Learning in a Heterogeneous Environment](https://mlsanity.com/paper/1906.06629/) (2019)
- [Over-the-Air Federated Learning from Heterogeneous Data](https://mlsanity.com/paper/2009.12787/) (2020)
- [Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control](https://mlsanity.com/paper/2006.05459/) (2020)

## Built on by (most cited first, 30 of 49)

- [Joint Privacy Enhancement and Quantization in Federated Learning](https://mlsanity.com/paper/2208.10888/) (2022)
- [FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2210.07615/) (2022)
- [Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks](https://mlsanity.com/paper/2212.07356/) (2022)
- [Accelerated Gradient Descent Learning over Multiple Access Fading Channels](https://mlsanity.com/paper/2107.12452/) (2021)
- [Scalable Hierarchical Over-the-Air Federated Learning](https://mlsanity.com/paper/2211.16162/) (2022)
- [Decentralized Low-Latency Collaborative Inference via Ensembles on the Edge](https://mlsanity.com/paper/2206.03165/) (2022)
- [Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Framework](https://mlsanity.com/paper/2503.14353/) (2025)
- [Learning in Restless Bandits under Exogenous Global Markov Process](https://mlsanity.com/paper/2112.09484/) (2021)
- [Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference](https://mlsanity.com/paper/2305.05933/) (2023)
- [Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks](https://mlsanity.com/paper/2110.14541/) (2021)
- [Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2303.10373/) (2023)
- [Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning](https://mlsanity.com/paper/2107.11415/) (2021)
- [Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach](https://mlsanity.com/paper/2405.12046/) (2024)
- [Bayesian Federated Inference for estimating Statistical Models based on Non-shared Multicenter Data sets](https://mlsanity.com/paper/2302.07677/) (2023)
- [Decentralized Federated Learning Over Imperfect Communication Channels](https://mlsanity.com/paper/2405.12894/) (2024)
- [Restless Multi-Armed Bandits under Exogenous Global Markov Process](https://mlsanity.com/paper/2202.13665/) (2022)
- [Graph Federated Learning for CIoT Devices in Smart Home Applications](https://mlsanity.com/paper/2212.14395/) (2022)
- [Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing](https://mlsanity.com/paper/2309.08477/) (2023)
- [Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates](https://mlsanity.com/paper/2403.18375/) (2024)
- [Communication-Efficient Stochastic Distributed Learning](https://mlsanity.com/paper/2501.13516/) (2025)
- [Deep Equilibrium Models Meet Federated Learning](https://mlsanity.com/paper/2305.18646/) (2023)
- [Enhancing Privacy in Federated Learning through Local Training](https://mlsanity.com/paper/2403.17572/) (2024)
- [Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing](https://mlsanity.com/paper/2512.03719/) (2025)
- [Federated learning and next generation wireless communications: A survey on bidirectional relationship](https://mlsanity.com/paper/2110.07649/) (2021)
- [Robust Online Learning over Networks](https://mlsanity.com/paper/2309.00520/) (2023)
- [Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM](https://mlsanity.com/paper/2309.14353/) (2023)
- [Sparse Training for Federated Learning with Regularized Error Correction](https://mlsanity.com/paper/2312.13795/) (2023)
- [Bayesian Federated Inference for Survival Models](https://mlsanity.com/paper/2404.17464/) (2024)
- [Matching Pursuit Based Scheduling for Over-the-Air Federated Learning](https://mlsanity.com/paper/2206.06679/) (2022)
- [Online Distributed Learning with Quantized Finite-Time Coordination](https://mlsanity.com/paper/2307.06620/) (2023)

## Abstract (arXiv)

The dramatic success of deep learning is largely due to the availability of data. Data samples are often acquired on edge devices, such as smart phones, vehicles and sensors, and in some cases cannot be shared due to privacy considerations. Federated learning is an emerging machine learning paradigm for training models across multiple edge devices holding local datasets, without explicitly exchanging the data. Learning in a federated manner differs from conventional centralized machine learning, and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications. Consequently, dedicated schemes derived from these areas are expected to play an important role in the success of federated learning and the transition of deep learning from the domain of centralized servers to mobile edge devices. In this article, we provide a unified systematic framework for federated learning in a manner that encapsulates and highlights the main challenges that are natural to treat using signal processing tools. We present a formulation for the federated learning paradigm from a signal processing perspective, and survey a set of candidate approaches for tackling its unique challenges. We further provide guidelines for the design and adaptation of signal processing and communication methods to facilitate federated learning at large scale.