mlsanityData to 7 Oct 2026

Federated Learning Challenges and Opportunities: An Outlook

arXiv 2202.00807 ↗First posted 1 Feb 2022cs.LG cs.AI cs.DCcited by 19

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Abstract (arXiv)

Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce development costs. Although many methods and applications have been developed for FL, several critical challenges for practical FL systems remain unaddressed. This paper provides an outlook on FL development, categorized into five emerging directions of FL, namely algorithm foundation, personalization, hardware and security constraints, lifelong learning, and nonstandard data. Our unique perspectives are backed by practical observations from large-scale federated systems for edge devices.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 1 Feb 2022.

Builds on

19 of 28 references.

References · 19

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2020Adaptive Personalized Federated Learningcited by 353
  7. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  8. 2021A Field Guide to Federated Optimizationcited by 312
  9. 2020Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learningcited by 78
  10. 2020Siloed Federated Learning for Multi-Centric Histopathology Datasetscited by 61
  11. 2021Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learningcited by 38
  12. 2018Model Selection Techniques -- An Overviewcited by 36
7 more
  1. 2020Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Searchcited by 30
  2. 2018Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learningcited by 15
  3. 2020Information Laundering for Model Privacycited by 13
  4. 2019Is a Classification Procedure Good Enough? A Goodness-of-Fit Assessment Tool for Classification Learningcited by 9
  5. 2020On Statistical Efficiency in Learningcited by 4
  6. 2021Interval Privacy: A Framework for Privacy-Preserving Data Collectioncited by 3
  7. 2016SLANTS: Sequential Adaptive Nonlinear Modeling of Vector Time Seriescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 7; 2024: 3; 2025: 2; 2026: 1.

19 papers.

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  1. 2023Pruning Deep Neural Networks from a Sparsity Perspectivecited by 25
  2. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  3. 2022Self-Aware Personalized Federated Learningcited by 16
  4. 2023FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropoutcited by 16
  5. 2023Demystifying Poisoning Backdoor Attacks from a Statistical Perspectivecited by 10
  6. 2022SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligencecited by 7
  7. 2023FedJudge: Federated Legal Large Language Modelcited by 5
  8. 2025Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigationcited by 5
  9. 2022Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusterscited by 3
  10. 2025Debunking Optimization Myths in Federated Learning for Medical Image Classificationcited by 3
  11. 2023A Framework for Incentivized Collaborative Learningcited by 2
  12. 2024Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?cited by 2
7 more
  1. 2022FedVMR: A New Federated Learning method for Video Moment Retrievalcited by 1
  2. 2023Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarizationcited by 1
  3. 2024DynamicFL: Federated Learning with Dynamic Communication Resource Allocationcited by 1
  4. 2022Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneitycited by 0
  5. 2022Reducing Impacts of System Heterogeneity in Federated Learning using Weight Update Magnitudescited by 0
  6. 2024Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networkscited by 0
  7. 2026Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numberscited by 0