# Federated Continual Learning for Edge-AI: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2411.13740 · HTML: https://mlsanity.com/paper/2411.13740/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 170 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models](https://mlsanity.com/paper/2308.06522/) (2023)
- [No One Left Behind: Real-World Federated Class-Incremental Learning](https://mlsanity.com/paper/2302.00903/) (2023)
- [Federated Continual Learning via Knowledge Fusion: A Survey](https://mlsanity.com/paper/2312.16475/) (2023)
- [A distillation-based approach integrating continual learning and federated learning for pervasive services](https://mlsanity.com/paper/2109.04197/) (2021)
- [pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning](https://mlsanity.com/paper/2310.13283/) (2023)
- [Towards Federated Learning on Time-Evolving Heterogeneous Data](https://mlsanity.com/paper/2112.13246/) (2021)
- [Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning](https://mlsanity.com/paper/2309.01289/) (2023)
- [FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients](https://mlsanity.com/paper/2311.11227/) (2023)
- [Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification](https://mlsanity.com/paper/2204.13591/) (2022)
- [Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal](https://mlsanity.com/paper/2110.09695/) (2021)
- [Federated Continual Learning for Text Classification via Selective Inter-client Transfer](https://mlsanity.com/paper/2210.06101/) (2022)
- [Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning](https://mlsanity.com/paper/2304.05260/) (2023)
- [FedYolo: Augmenting Federated Learning with Pretrained Transformers](https://mlsanity.com/paper/2307.04905/) (2023)
- [FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning](https://mlsanity.com/paper/2007.10560/) (2020)
- [Continual Adaptation of Vision Transformers for Federated Learning](https://mlsanity.com/paper/2306.09970/) (2023)
- [Federated Class-Incremental Learning with Prompting](https://mlsanity.com/paper/2310.08948/) (2023)
- [Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing](https://mlsanity.com/paper/2210.15051/) (2022)
- [A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions](https://mlsanity.com/paper/2306.02037/) (2023)
- [Federated and continual learning for classification tasks in a society of devices](https://mlsanity.com/paper/2006.07129/) (2020)
- [Failure-Sentient Composition For Swarm-Based Drone Services](https://mlsanity.com/paper/2305.13892/) (2023)
- [Masked Autoencoders are Efficient Continual Federated Learners](https://mlsanity.com/paper/2306.03542/) (2023)

## Built on by (most cited first, 11 of 11)

- [Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey](https://mlsanity.com/paper/2412.13840/) (2024)
- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [Federated Continual Learning: Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2502.07059/) (2025)
- [Resource-Constrained Federated Continual Learning: What Does Matter?](https://mlsanity.com/paper/2501.08737/) (2025)
- [Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning](https://mlsanity.com/paper/2505.12318/) (2025)
- [FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning](https://mlsanity.com/paper/2509.07342/) (2025)
- [C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift](https://mlsanity.com/paper/2606.18003/) (2026)
- [Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learning](https://mlsanity.com/paper/2506.18789/) (2025)
- [Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning](https://mlsanity.com/paper/2508.05157/) (2025)
- [PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks](https://mlsanity.com/paper/2605.19145/) (2026)
- [When the Cross-Silo Federation Goes Offline: Continual Learning for Site Onboarding with Limited Unlabeled Data](https://mlsanity.com/paper/2610.05598/) (2026)

## Abstract (arXiv)

Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users. In Edge-AI, federated continual learning (FCL) has emerged as an imperative framework, which fuses knowledge from different clients while preserving data privacy and retaining knowledge from previous tasks as it learns new ones. By so doing, FCL aims to ensure stable and reliable performance of learning models in dynamic and distributed environments. In this survey, we thoroughly review the state-of-the-art research and present the first comprehensive survey of FCL for Edge-AI. We categorize FCL methods based on three task characteristics: federated class continual learning, federated domain continual learning, and federated task continual learning. For each category, an in-depth investigation and review of the representative methods are provided, covering background, challenges, problem formalisation, solutions, and limitations. Besides, existing real-world applications empowered by FCL are reviewed, indicating the current progress and potential of FCL in diverse application domains. Furthermore, we discuss and highlight several prospective research directions of FCL such as algorithm-hardware co-design for FCL and FCL with foundation models, which could provide insights into the future development and practical deployment of FCL in the era of Edge-AI.