# Federated Learning Challenges and Opportunities: An Outlook

arXiv: https://arxiv.org/abs/2202.00807 · HTML: https://mlsanity.com/paper/2202.00807/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 19 of 19)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://mlsanity.com/paper/2002.04156/) (2020)
- [Siloed Federated Learning for Multi-Centric Histopathology Datasets](https://mlsanity.com/paper/2008.07424/) (2020)
- [Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning](https://mlsanity.com/paper/2106.03328/) (2021)
- [Model Selection Techniques -- An Overview](https://mlsanity.com/paper/1810.09583/) (2018)
- [Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search](https://mlsanity.com/paper/2004.08546/) (2020)
- [Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning](https://mlsanity.com/paper/1805.05838/) (2018)
- [Information Laundering for Model Privacy](https://mlsanity.com/paper/2009.06112/) (2020)
- [Is a Classification Procedure Good Enough? A Goodness-of-Fit Assessment Tool for Classification Learning](https://mlsanity.com/paper/1911.03063/) (2019)
- [On Statistical Efficiency in Learning](https://mlsanity.com/paper/2012.13307/) (2020)
- [Interval Privacy: A Framework for Privacy-Preserving Data Collection](https://mlsanity.com/paper/2106.09565/) (2021)
- [SLANTS: Sequential Adaptive Nonlinear Modeling of Vector Time Series](https://mlsanity.com/paper/1610.02725/) (2016)

## Built on by (most cited first, 19 of 19)

- [Pruning Deep Neural Networks from a Sparsity Perspective](https://mlsanity.com/paper/2302.05601/) (2023)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [Self-Aware Personalized Federated Learning](https://mlsanity.com/paper/2204.08069/) (2022)
- [FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout](https://mlsanity.com/paper/2307.02623/) (2023)
- [Demystifying Poisoning Backdoor Attacks from a Statistical Perspective](https://mlsanity.com/paper/2310.10780/) (2023)
- [SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence](https://mlsanity.com/paper/2203.08176/) (2022)
- [FedJudge: Federated Legal Large Language Model](https://mlsanity.com/paper/2309.08173/) (2023)
- [Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation](https://mlsanity.com/paper/2501.18416/) (2025)
- [Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters](https://mlsanity.com/paper/2205.09470/) (2022)
- [Debunking Optimization Myths in Federated Learning for Medical Image Classification](https://mlsanity.com/paper/2507.19822/) (2025)
- [A Framework for Incentivized Collaborative Learning](https://mlsanity.com/paper/2305.17052/) (2023)
- [Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?](https://mlsanity.com/paper/2411.06618/) (2024)
- [FedVMR: A New Federated Learning method for Video Moment Retrieval](https://mlsanity.com/paper/2210.15977/) (2022)
- [Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization](https://mlsanity.com/paper/2304.11524/) (2023)
- [DynamicFL: Federated Learning with Dynamic Communication Resource Allocation](https://mlsanity.com/paper/2409.04986/) (2024)
- [Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity](https://mlsanity.com/paper/2207.00392/) (2022)
- [Reducing Impacts of System Heterogeneity in Federated Learning using Weight Update Magnitudes](https://mlsanity.com/paper/2208.14808/) (2022)
- [Generative AI-Powered Plugin for Robust Federated Learning in Heterogeneous IoT Networks](https://mlsanity.com/paper/2410.23824/) (2024)
- [Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers](https://mlsanity.com/paper/2603.12684/) (2026)

## 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.