mlsanityData to 14 Oct 2026

Federated Learning: A Signal Processing Perspective

arXiv 2103.17150 ↗First posted 31 Mar 2021eess.SP cs.LGcited by 49

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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.

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

39 of 73 references.

References · 39

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2019Deep Leakage from Gradientscited by 1,021
  7. 2019Towards Federated Learning at Scale: System Designcited by 832
  8. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  9. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  10. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  11. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  12. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
27 more
  1. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  2. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 478
  3. 2019Agnostic Federated Learningcited by 456
  4. 2018Sparsified SGD with Memorycited by 376
  5. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  6. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  7. 2018Federated Learning via Over-the-Air Computationcited by 219
  8. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  9. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  10. 2019Federated Learning over Wireless Fading Channelscited by 183
  11. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  12. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  13. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 107
  14. 2018Generalized Byzantine-tolerant SGDcited by 95
  15. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 94
  16. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 90
  17. 2020Over-the-Air Federated Learning from Heterogeneous Datacited by 73
  18. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 63
  19. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 62
  20. 2017Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descentcited by 59
  21. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 57
  22. 2020Design and Analysis of Uplink and Downlink Communications for Federated Learningcited by 48
  23. 2020Wireless Communications for Collaborative Federated Learningcited by 47
  24. 2020Blind Federated Edge Learningcited by 43
  25. 2019Over-the-Air Computation Systems: Optimization, Analysis and Scaling Lawscited by 27
  26. 2021Distributed Learning over Markovian Fading Channels for Stable Spectrum Accesscited by 13
  27. 2020Bayesian Federated Learning over Wireless Networkscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 9; 2023: 10; 2024: 12; 2025: 9; 2026: 3.

49 papers.

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  1. 2022Joint Privacy Enhancement and Quantization in Federated Learningcited by 35
  2. 2022FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learningcited by 27
  3. 2022Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networkscited by 20
  4. 2021Accelerated Gradient Descent Learning over Multiple Access Fading Channelscited by 14
  5. 2022Scalable Hierarchical Over-the-Air Federated Learningcited by 14
  6. 2022Decentralized Low-Latency Collaborative Inference via Ensembles on the Edgecited by 10
  7. 2025Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Frameworkcited by 10
  8. 2021Learning in Restless Bandits under Exogenous Global Markov Processcited by 9
  9. 2023Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interferencecited by 9
  10. 2021Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networkscited by 8
  11. 2023Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approachcited by 8
  12. 2021Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learningcited by 7
37 more
  1. 2024Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approachcited by 6
  2. 2023Bayesian Federated Inference for estimating Statistical Models based on Non-shared Multicenter Data setscited by 5
  3. 2024Decentralized Federated Learning Over Imperfect Communication Channelscited by 5
  4. 2022Restless Multi-Armed Bandits under Exogenous Global Markov Processcited by 4
  5. 2022Graph Federated Learning for CIoT Devices in Smart Home Applicationscited by 4
  6. 2023Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testingcited by 4
  7. 2024Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updatescited by 4
  8. 2025Communication-Efficient Stochastic Distributed Learningcited by 4
  9. 2023Deep Equilibrium Models Meet Federated Learningcited by 3
  10. 2024Enhancing Privacy in Federated Learning through Local Trainingcited by 3
  11. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processingcited by 3
  12. 2021Federated learning and next generation wireless communications: A survey on bidirectional relationshipcited by 2
  13. 2023Robust Online Learning over Networkscited by 2
  14. 2023Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMMcited by 2
  15. 2023Sparse Training for Federated Learning with Regularized Error Correctioncited by 2
  16. 2024Bayesian Federated Inference for Survival Modelscited by 2
  17. 2022Matching Pursuit Based Scheduling for Over-the-Air Federated Learningcited by 1
  18. 2023Online Distributed Learning with Quantized Finite-Time Coordinationcited by 1
  19. 2024Boosting Fairness and Robustness in Over-the-Air Federated Learningcited by 1
  20. 2024Gaussian Process Upper Confidence Bounds in Distributed Point Target Tracking over Wireless Sensor Networkscited by 1
  21. 2024Noise-Robust and Resource-Efficient ADMM-based Federated Learningcited by 1
  22. 2024Federated Learning for UAV-Based Spectrum Sensing: Enhancing Accuracy Through SNR-Weighted Model Aggregationcited by 1
  23. 2025PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learningcited by 1
  24. 2025Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networkscited by 1
  25. 2025FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphscited by 1
  26. 2025Adaptive Deadline and Batch Layered Synchronized Federated Learningcited by 1
  27. 2025OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learningcited by 1
  28. 2021Federated Learning with Superquantile Aggregation for Heterogeneous Datacited by 0
  29. 2022Deep Unfolding-based Weighted Averaging for Federated Learning in Heterogeneous Environmentscited by 0
  30. 2023A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systemscited by 0
  31. 2024Bayesian Federated Inference for regression models based on non-shared multicenter data sets from heterogeneous populationscited by 0
  32. 2024Federated Learning with Multi-resolution Model Broadcastcited by 0
  33. 2024Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysiscited by 0
  34. 2025Privacy-Preserving Quantized Federated Learning with Diverse Precisioncited by 0
  35. 2026Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharingcited by 0
  36. 2026Federated Learning with Hypergradient-based Online Update of Aggregation Weightscited by 0
  37. 2026Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learningcited by 0