Non-iid data in federated learning: A systematic review with taxonomy, metrics, methods, frameworks and future directions
D Solans, M Heikkila, A Vitaletti, N Kourtellis… - ar** their data locally. However, existing FL approaches typically assume that the data in …
[HTML][HTML] Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?
The integration of AI and ML into energy forecasting is crucial for modern energy
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
Diffusion-driven data replay: A novel approach to combat forgetting in federated class continual learning
Abstract Federated Class Continual Learning (FCCL) merges the challenges of distributed
client learning with the need for seamless adaptation to new classes without forgetting old …
client learning with the need for seamless adaptation to new classes without forgetting old …
Unleashing the power of continual learning on non-centralized devices: A survey
Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for
enabling distributed devices such as vehicles and servers to handle streaming data from a …
enabling distributed devices such as vehicles and servers to handle streaming data from a …
Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning
This paper delves into federated class-incremental learning (FCiL), where new classes
appear continually or even privately to local clients. However, existing FCiL methods suffer …
appear continually or even privately to local clients. However, existing FCiL methods suffer …
Rehearsal-free continual federated learning with synergistic regularization
Continual Federated Learning (CFL) allows distributed devices to collaboratively learn novel
concepts from continuously shifting training data while avoiding knowledge forgetting of …
concepts from continuously shifting training data while avoiding knowledge forgetting of …
A multifaceted survey on federated learning: Fundamentals, paradigm shifts, practical issues, recent developments, partnerships, trade-offs, trustworthiness, and ways …
A Majeed, SO Hwang - IEEE Access, 2024 - ieeexplore.ieee.org
Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …
environments because it does not require data to be aggregated in some central place to …