Copertina

Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zaland, Obaidullah, Khan, Zulfiqar Ahmad, Bhuyan, Monowar
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
di: Zaland, Obaidullah, et al.
Pubblicazione: (2026)

Edge AI in Highly Volatile Environments: Is Fairness Worth the Accuracy Trade-off?
di: Zaland, Obaidullah, et al.
Pubblicazione: (2025)

Federated Learning Under Temporal Drift -- Mitigating Catastrophic Forgetting via Experience Replay
di: Kokkula, Sahasra, et al.
Pubblicazione: (2026)

Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
di: Huo, Yujia, et al.
Pubblicazione: (2025)

Revisiting Ensembling in One-Shot Federated Learning
di: Allouah, Youssef, et al.
Pubblicazione: (2024)

Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learning
di: Song, Haoyue, et al.
Pubblicazione: (2024)

FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
di: Jhunjhunwala, Divyansh, et al.
Pubblicazione: (2024)

Towards Efficient Replay in Federated Incremental Learning
di: Li, Yichen, et al.
Pubblicazione: (2024)

Silent Failures in Stateless Systems: Rethinking Anomaly Detection for Serverless Computing
di: Nguyen, Chanh, et al.
Pubblicazione: (2025)

DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning
di: Luo, Kangyang, et al.
Pubblicazione: (2024)

FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning
di: Yoon, Taehwan, et al.
Pubblicazione: (2025)

On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients
di: Keshri, Satish Kumar, et al.
Pubblicazione: (2024)

ToFU: Transforming How Federated Learning Systems Forget User Data
di: Tran, Van-Tuan, et al.
Pubblicazione: (2025)

Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching
di: Li, Yichen, et al.
Pubblicazione: (2024)

One-Shot Federated Learning with Bayesian Pseudocoresets
di: d'Hondt, Tim, et al.
Pubblicazione: (2024)

Taming Cold Starts: Proactive Serverless Scheduling with Model Predictive Control
di: Nguyen, Chanh, et al.
Pubblicazione: (2025)

FUPareto: Bridging the Forgetting-Utility Gap in Federated Unlearning via Pareto Augmented Optimization
di: Wang, Zeyan, et al.
Pubblicazione: (2026)

Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
di: Khan, Ahmad Faraz, et al.
Pubblicazione: (2022)

Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
di: Bouchiha, Mouhamed Amine, et al.
Pubblicazione: (2026)

A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning
di: Bai, Jun, et al.
Pubblicazione: (2024)

FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
di: Feng, Xue, et al.
Pubblicazione: (2025)

One-Shot Averaging for Distributed TD($λ$) Under Markov Sampling
di: Tian, Haoxing, et al.
Pubblicazione: (2024)

Sparse Incremental Aggregation in Multi-Hop Federated Learning
di: Mukherjee, Sourav, et al.
Pubblicazione: (2024)

FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices
di: Yao, Dezhong, et al.
Pubblicazione: (2025)

On the Byzantine-Resilience of Distillation-Based Federated Learning
di: Roux, Christophe, et al.
Pubblicazione: (2024)

Partial Federated Learning
di: Feng, Tiantian, et al.
Pubblicazione: (2024)

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
di: Ji, Shaoxiong, et al.
Pubblicazione: (2021)

Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning
di: Egger, Maximilian, et al.
Pubblicazione: (2025)

Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
di: Xiong, Guojun, et al.
Pubblicazione: (2023)

FLStore: Efficient Federated Learning Storage for non-training workloads
di: Khan, Ahmad Faraz, et al.
Pubblicazione: (2025)

One-Shot Federated Learning with Classifier-Free Diffusion Models
di: Zaland, Obaidullah, et al.
Pubblicazione: (2025)

Blockchain-Enabled Federated Learning
di: Rangwala, Murtaza, et al.
Pubblicazione: (2025)

Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
di: Fenoglio, Dario, et al.
Pubblicazione: (2024)

Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
di: Ma, Mengmeng, et al.
Pubblicazione: (2024)

Federated One-Shot Learning with Data Privacy and Objective-Hiding
di: Egger, Maximilian, et al.
Pubblicazione: (2025)

Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning
di: Xia, Yue, et al.
Pubblicazione: (2025)

EcoLearn: Optimizing the Carbon Footprint of Federated Learning
di: Mehboob, Talha, et al.
Pubblicazione: (2023)

Federated Graph Learning with Graphless Clients
di: Fu, Xingbo, et al.
Pubblicazione: (2024)

Empowering Data Mesh with Federated Learning
di: Li, Haoyuan, et al.
Pubblicazione: (2024)

Disentangling data distribution for Federated Learning
di: Zhao, Xinyuan, et al.
Pubblicazione: (2024)