Publication
Asynchronous federated learning for web-based OCT image analysis
Hasan Md Tusfiqur Alam; Tim Maurer; Abdulrahman Mohamed Selim; Matthias Eiletz; Michael Barz; Daniel Sonntag
In: Journal of Medical Imaging (JMI), Vol. 13, No. 1, Pages 014501-014501, Society of Photo-Optical Instrumentation Engineers, 1/2026.
Abstract
Purpose: Centralized machine learning often struggles with limited data access and expert involvement. This
study investigates decentralized approaches that preserve data privacy while enabling collaborative model training for
medical imaging tasks.
Approach: We explore asynchronous Federated Learning (FL) using the FedBuff algorithm for classifying Op-
tical Coherence Tomography (OCT) retina images. Unlike synchronous algorithms like FedAvg, which require all
clients to participate simultaneously, FedBuff supports independent client updates. We compare its performance
to both centralized models and FedAvg. Additionally, we develop a browser-based proof-of-concept system using
modern web technologies to assess the feasibility and limitations of interactive, collaborative learning in real-world
settings.
Results: FedBuff performs well in binary OCT classification tasks but shows reduced accuracy in more complex,
multi-class scenarios. FedAvg achieves results comparable to centralized training, consistent with previous findings.
While FedBuff underperforms compared to FedAvg and centralized models, it still delivers acceptable accuracy in
less complex settings. The browser-based prototype demonstrates the potential for accessible, user-driven FL sys-
tems but also highlights technical limitations in current web standards, especially regarding local computation and
communication efficiency.
Conclusions: Asynchronous FL via FedBuff offers a promising, privacy-preserving approach for medical image
classification, particularly when synchronous participation is impractical. However, its scalability to complex classi-
fication tasks remains limited. Web-based implementations have the potential to broaden access to collaborative AI
tools, but limitations of the current technologies need to be further investigated.
Projects
- MPEER - Virtuelle Musikberatung im Semantic Web
- No-IDLE - Interactive Deep Learning Enterprise
- NoIDLEChatGPT - No-IDLE meets ChatGPT
