Federated Learning Under Temporal Drift -- Mitigating Catastrophic Forgetting via Experience Replay
Fuente:
arXiv
Saved in:
| Main Authors: | Kokkula, Sahasra, David, Daniel, Baruah, Aaditya |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Future-Proofing IoT: Unleashing the Power of AWS Greengrass in Propelling Smart Devices to New Heights
by: Kokkula, Sahasra, et al.
Published: (2024)
by: Kokkula, Sahasra, et al.
Published: (2024)
Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
by: Huo, Yujia, et al.
Published: (2025)
by: Huo, Yujia, et al.
Published: (2025)
Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning
by: Zaland, Obaidullah, et al.
Published: (2026)
by: Zaland, Obaidullah, et al.
Published: (2026)
FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning
by: Yoon, Taehwan, et al.
Published: (2025)
by: Yoon, Taehwan, et al.
Published: (2025)
Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learning
by: Song, Haoyue, et al.
Published: (2024)
by: Song, Haoyue, et al.
Published: (2024)
Towards Efficient Replay in Federated Incremental Learning
by: Li, Yichen, et al.
Published: (2024)
by: Li, Yichen, et al.
Published: (2024)
FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
by: Bai, Yuxuan, et al.
Published: (2025)
by: Bai, Yuxuan, et al.
Published: (2025)
Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?
by: Mei, Yongsheng, et al.
Published: (2024)
by: Mei, Yongsheng, et al.
Published: (2024)
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
by: Shen, Tao, et al.
Published: (2025)
by: Shen, Tao, et al.
Published: (2025)
Drift-Aware Federated Learning: A Causal Perspective
by: Fang, Yunjie, et al.
Published: (2025)
by: Fang, Yunjie, et al.
Published: (2025)
An Information-Theoretic Analysis for Federated Learning under Concept Drift
by: Peng, Fu, et al.
Published: (2025)
by: Peng, Fu, et al.
Published: (2025)
ToFU: Transforming How Federated Learning Systems Forget User Data
by: Tran, Van-Tuan, et al.
Published: (2025)
by: Tran, Van-Tuan, et al.
Published: (2025)
FUPareto: Bridging the Forgetting-Utility Gap in Federated Unlearning via Pareto Augmented Optimization
by: Wang, Zeyan, et al.
Published: (2026)
by: Wang, Zeyan, et al.
Published: (2026)
STHFL: Spatio-Temporal Heterogeneous Federated Learning
by: Guo, Shunxin, et al.
Published: (2025)
by: Guo, Shunxin, et al.
Published: (2025)
Federated Temporal Graph Clustering
by: Zhou, Zihao, et al.
Published: (2024)
by: Zhou, Zihao, et al.
Published: (2024)
Flashback: Understanding and Mitigating Forgetting in Federated Learning
by: Aljahdali, Mohammed, et al.
Published: (2024)
by: Aljahdali, Mohammed, et al.
Published: (2024)
Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics
by: Xiang, Ming, et al.
Published: (2024)
by: Xiang, Ming, et al.
Published: (2024)
Fault-Tolerant Decentralized Distributed Asynchronous Federated Learning with Adaptive Termination Detection
by: Akkinepally, Phani Sahasra, et al.
Published: (2025)
by: Akkinepally, Phani Sahasra, et al.
Published: (2025)
Mitigating Temporal Blindness in Kubernetes Autoscaling: An Attention-Double-LSTM Framework
by: Shaikh, Faraz, et al.
Published: (2026)
by: Shaikh, Faraz, et al.
Published: (2026)
Adaptive Compression in Federated Learning via Side Information
by: Isik, Berivan, et al.
Published: (2023)
by: Isik, Berivan, et al.
Published: (2023)
Adaptive Federated Learning via New Entropy Approach
by: Zheng, Shensheng, et al.
Published: (2023)
by: Zheng, Shensheng, et al.
Published: (2023)
Personalized Federated Learning via ADMM with Moreau Envelope
by: Zhu, Shengkun, et al.
Published: (2023)
by: Zhu, Shengkun, et al.
Published: (2023)
Federated Time Series Generation on Feature and Temporally Misaligned Data
by: Soi, Zhi Wen, et al.
Published: (2024)
by: Soi, Zhi Wen, et al.
Published: (2024)
EcoLearn: Optimizing the Carbon Footprint of Federated Learning
by: Mehboob, Talha, et al.
Published: (2023)
by: Mehboob, Talha, et al.
Published: (2023)
Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays
by: Iakovidou, Charikleia, et al.
Published: (2024)
by: Iakovidou, Charikleia, et al.
Published: (2024)
Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization
by: Guo, Kun, et al.
Published: (2025)
by: Guo, Kun, et al.
Published: (2025)
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
by: Li, Shiwei, et al.
Published: (2024)
by: Li, Shiwei, et al.
Published: (2024)
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training
by: Wu, Yebo, et al.
Published: (2024)
by: Wu, Yebo, et al.
Published: (2024)
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
by: Tian, Chunlin, et al.
Published: (2024)
by: Tian, Chunlin, et al.
Published: (2024)
Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging
by: Theologitis, Michail, et al.
Published: (2024)
by: Theologitis, Michail, et al.
Published: (2024)
Reinforcement Learning-based Adaptive Mitigation of Uncorrected DRAM Errors in the Field
by: Boixaderas, Isaac, et al.
Published: (2024)
by: Boixaderas, Isaac, et al.
Published: (2024)
Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-Temporal Graph Learning Method for Traffic Flow Forecasting
by: Wang, Feng, et al.
Published: (2025)
by: Wang, Feng, et al.
Published: (2025)
Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
by: Li, Qiyue, et al.
Published: (2025)
by: Li, Qiyue, et al.
Published: (2025)
Drift Detection: Introducing Gaussian Split Detector
by: Fuccellaro, Maxime, et al.
Published: (2024)
by: Fuccellaro, Maxime, et al.
Published: (2024)
FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning
by: Stricker, Fabian, et al.
Published: (2026)
by: Stricker, Fabian, et al.
Published: (2026)
Partial Federated Learning
by: Feng, Tiantian, et al.
Published: (2024)
by: Feng, Tiantian, et al.
Published: (2024)
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data
by: Amalanshu, Avi, et al.
Published: (2024)
by: Amalanshu, Avi, et al.
Published: (2024)
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
by: Ji, Shaoxiong, et al.
Published: (2021)
by: Ji, Shaoxiong, et al.
Published: (2021)
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
by: Li, Zhe, et al.
Published: (2024)
by: Li, Zhe, et al.
Published: (2024)
FedBiF: Communication-Efficient Federated Learning via Bits Freezing
by: Li, Shiwei, et al.
Published: (2025)
by: Li, Shiwei, et al.
Published: (2025)
Similar Items
-
Future-Proofing IoT: Unleashing the Power of AWS Greengrass in Propelling Smart Devices to New Heights
by: Kokkula, Sahasra, et al.
Published: (2024) -
Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
by: Huo, Yujia, et al.
Published: (2025) -
Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning
by: Zaland, Obaidullah, et al.
Published: (2026) -
FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning
by: Yoon, Taehwan, et al.
Published: (2025) -
Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learning
by: Song, Haoyue, et al.
Published: (2024)