A Systematic Literature Review on the Use of Federated Learning and Bioinspired Computing

被引:1
|
作者
de Souza, Rafael Marin Machado [1 ,2 ]
Holm, Andrew [3 ]
Biczyk, Marcio [2 ]
de Castro, Leandro Nunes [1 ,2 ,3 ]
机构
[1] State Univ Campinas Unicamp, Sch Technol, R Paschoal Marmo, 1888-Jd Nova Italia, BR-13484332 Limeira, SP, Brazil
[2] Univ Sao Paulo, Clin Hosp, Med Fac, In lab InovaHC, R Dr Ovidio Pires Campos, 75-Cerqueira Cesar, BR-05401000 Sao Paulo, SP, Brazil
[3] Florida Gulf Coast Univ FGCU, Dept Comp & Software Engn, 10501 Fgcu Blvd S, Ft Myers, FL 33965 USA
基金
巴西圣保罗研究基金会;
关键词
federated learning; bioinspired computing; natural computing; evolutionary algorithm; particle swarm optimization;
D O I
10.3390/electronics13163157
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary fields, have gained significant attention in the machine learning community due to their unique characteristics. FL enables decentralized machine learning by allowing models to be trained on data residing across multiple devices or servers without exchanging raw data, thus enhancing privacy and reducing communication overhead. Conversely, BIC draws inspiration from nature to develop robust and adaptive computational solutions for complex problems. This paper explores the state of the art in the integration of FL and BIC, introducing BIC techniques and discussing the motivations for their integration with FL. The convergence of these fields can lead to improved model accuracy, enhanced privacy, energy efficiency, and reduced communication overhead. This synergy addresses inherent challenges in FL, such as data heterogeneity and limited computational resources, and opens up new avenues for developing more efficient and autonomous learning systems. The integration of FL and BIC holds promise for various application domains, including healthcare, finance, and smart cities, where privacy-preserving and efficient computation is paramount. This survey provides a systematic review of the current research landscape, identifies key challenges and opportunities, and suggests future directions for the successful integration of FL and BIC.
引用
收藏
页数:25
相关论文
共 50 条
  • [31] Dependable Fog Computing: A Systematic Literature Review
    Bakhshi, Zeinab
    Rodriguez-Navas, Guillermo
    Hansson, Hans
    2019 45TH EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2019), 2019, : 395 - 403
  • [32] Quantum Computing in The Cloud - A Systematic Literature Review
    Zhahir, Amirul Asyraf
    Mohd, Siti Munirah
    Shuhud, Mohd Ilias M.
    Idrus, Bahari
    Zainuddin, Hishamuddin
    Jan, Nurhidaya Mohamad
    Wahiddin, Mohamed Ridza
    INTERNATIONAL JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING SYSTEMS, 2024, 15 (02) : 185 - 200
  • [33] A systematic literature review of mobile cloud computing
    Ibukun, Eweoya
    Daramola, Olawande
    International Journal of Multimedia and Ubiquitous Engineering, 2015, 10 (12): : 135 - 152
  • [34] Sustainability in Computing Education: A Systematic Literature Review
    Peters, Anne-Kathrin
    Capilla, Rafael
    Coroama, Vlad Constantin
    Heldal, Rogardt
    Lago, Patricia
    Leifler, Ola
    Moreira, Ana
    Fernandes, Joao Paulo
    Penzenstadler, Birgit
    Porras, Jari
    Venters, Colin C.
    ACM TRANSACTIONS ON COMPUTING EDUCATION, 2024, 24 (01)
  • [35] Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment
    Abimannan, Satheesh
    El-Alfy, El-Sayed M.
    Hussain, Shahid
    Chang, Yue-Shan
    Shukla, Saurabh
    Satheesh, Dhivyadharsini
    Breslin, John G.
    SUSTAINABILITY, 2023, 15 (18)
  • [36] Exploring the landscape of learning analytics privacy in fog and edge computing: A systematic literature review
    Amo-Filva, Daniel
    Fonseca, David
    Garcia-Penalvo, Francisco Jose
    Forment, Marc Alier
    Guerrero, Maria Jose Casany
    Godoy, Guillem
    COMPUTERS IN HUMAN BEHAVIOR, 2024, 158
  • [37] Machine Learning-Based Resource Management in Fog Computing: A Systematic Literature Review
    Khan, Fahim Ullah
    Shah, Ibrar Ali
    Jan, Sadaqat
    Ahmad, Shabir
    Whangbo, Taegkeun
    SENSORS, 2025, 25 (03)
  • [38] A systematic literature review on the use of machine learning in code clone research
    Kaur, Manpreet
    Rattan, Dhavleesh
    COMPUTER SCIENCE REVIEW, 2023, 47
  • [39] A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
    Watson, Cody
    Cooper, Nathan
    Palacio, David Nader
    Moran, Kevin
    Poshyvanyk, Denys
    ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 2022, 31 (02)
  • [40] The use of reinforcement learning algorithms in object tracking: A systematic literature review
    Barrientos, R. David J.
    Medina, Marie Chantelle C.
    Fernandes, Bruno J. T.
    Barros, Pablo V. A.
    NEUROCOMPUTING, 2024, 596