Federated Continual Learning for Edge-AI: A Comprehensive Survey

• URL: http://arxiv.org/abs/2411.13740v1
• Date: Wed, 20 Nov 2024 22:49:28 GMT
• Title: Federated Continual Learning for Edge-AI: A Comprehensive Survey
• Authors: Zi Wang, Fei Wu, Feng Yu, Yurui Zhou, Jia Hu, Geyong Min,
• Abstract summary: In Edge-AI, federated continual learning (FCL) has emerged as an imperative framework. FCL aims to ensure stable and reliable performance of learning models in dynamic and distributed environments. FCL methods based on three task characteristics: federated class continual learning, federated domain continual learning, and federated task continual learning.
• Score: 28.944063155195753
• License:
• Abstract: 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.

Related papers

• Federated Large Language Models: Current Progress and Future Directions [63.68614548512534]
  This paper surveys Federated learning for LLMs (FedLLM), highlighting recent advances and future directions. We focus on two key aspects: fine-tuning and prompt learning in a federated setting, discussing existing work and associated research challenges.
  arXiv  Detail & Related papers  (2024-09-24T04:14:33Z)
• From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models [56.9134620424985]
  Cross-modal reasoning (CMR) is increasingly recognized as a crucial capability in the progression toward more sophisticated artificial intelligence systems. The recent trend of deploying Large Language Models (LLMs) to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy.
  arXiv  Detail & Related papers  (2024-09-19T02:51:54Z)
• Recent Advances of Foundation Language Models-based Continual Learning: A Survey [31.171203978742447]
  Foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV) However, they can not emulate human-like continuous learning due to catastrophic forgetting. Various continual learning (CL)-based methodologies have been developed to refine LMs, enabling them to adapt to new tasks without forgetting previous knowledge.
  arXiv  Detail & Related papers  (2024-05-28T23:32:46Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• Federated Continual Learning via Knowledge Fusion: A Survey [33.74289759536269]
  Federated Continual Learning (FCL) is an emerging paradigm to address model learning in both federated and continual learning environments. The key objective of FCL is to fuse heterogeneous knowledge from different clients and retain knowledge of previous tasks while learning on new ones. In this work, we delineate federated learning and continual learning first and then discuss their integration, i.e., FCL, and particular FCL via knowledge fusion.
  arXiv  Detail & Related papers  (2023-12-27T08:47:39Z)
• A Survey on Federated Unlearning: Challenges, Methods, and Future Directions [21.90319100485268]
  In recent years, the notion of the right to be forgotten" (RTBF) has become a crucial aspect of data privacy for digital trust and AI safety. Machine unlearning (MU) has gained considerable attention which allows an ML model to selectively eliminate identifiable information. FU has emerged to confront the challenge of data erasure within federated learning settings.
  arXiv  Detail & Related papers  (2023-10-31T13:32:00Z)
• Federated Learning-Empowered AI-Generated Content in Wireless Networks [58.48381827268331]
  Federated learning (FL) can be leveraged to improve learning efficiency and achieve privacy protection for AIGC. We present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content.
  arXiv  Detail & Related papers  (2023-07-14T04:13:11Z)
• Hierarchically Structured Task-Agnostic Continual Learning [0.0]
  We take a task-agnostic view of continual learning and develop a hierarchical information-theoretic optimality principle. We propose a neural network layer, called the Mixture-of-Variational-Experts layer, that alleviates forgetting by creating a set of information processing paths. Our approach can operate in a task-agnostic way, i.e., it does not require task-specific knowledge, as is the case with many existing continual learning algorithms.
  arXiv  Detail & Related papers  (2022-11-14T19:53:15Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey [53.73359052511171]
  Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. We present a framework for curriculum learning (CL) in RL, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals.
  arXiv  Detail & Related papers  (2020-03-10T20:41:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.