Cover Image

Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bhope, Rahul Atul, Jayaram, K. R., Venkateswaran, Praveen, Venkatasubramanian, Nalini
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

OptiSeq: Ordering Examples On-The-Fly for In-Context Learning
by: Bhope, Rahul Atul, et al.
Published: (2025)

VOILA: Value-of-Information Guided Fidelity Selection for Cost-Aware Multimodal Question Answering
by: Bhope, Rahul Atul, et al.
Published: (2026)

DIM-SUM: Dynamic IMputation for Smart Utility Management
by: Hildebrant, Ryan, et al.
Published: (2025)

Theory on Mixture-of-Experts in Continual Learning
by: Li, Hongbo, et al.
Published: (2024)

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts
by: Dwivedi, Chaitanya, et al.
Published: (2026)

Dynamic Mixture of Experts Against Severe Distribution Shifts
by: Kim, Donghu
Published: (2025)

Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
by: Fang, Zihan, et al.
Published: (2026)

Mixtures of SubExperts for Large Language Continual Learning
by: Kang, Haeyong
Published: (2025)

HELLoRA: Hot Experts Layer-Level Low-Rank Adaptation for Mixture-of-Experts Models
by: Wei, Jia, et al.
Published: (2026)

SAME: Stabilized Mixture-of-Experts for Multimodal Continual Instruction Tuning
by: Xie, Zhen-Hao, et al.
Published: (2026)

dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
by: Xie, Luyuan, et al.
Published: (2025)

Mixture of Experts based Multi-task Supervise Learning from Crowds
by: Han, Tao, et al.
Published: (2024)

Mixture-of-Experts Meets In-Context Reinforcement Learning
by: Wu, Wenhao, et al.
Published: (2025)

Parameter-Efficient Routed Fine-Tuning: Mixture-of-Experts Demands Mixture of Adaptation Modules
by: Liu, Yilun, et al.
Published: (2025)

Mixture of Raytraced Experts
by: Perin, Andrea, et al.
Published: (2025)

PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning
by: Feng, Yu, et al.
Published: (2025)

Mixture of Experts in a Mixture of RL settings
by: Willi, Timon, et al.
Published: (2024)

Speculating Experts Accelerates Inference for Mixture-of-Experts
by: Madan, Vivan, et al.
Published: (2026)

MC#: Mixture Compressor for Mixture-of-Experts Large Models
by: Huang, Wei, et al.
Published: (2025)

Efficiently Editing Mixture-of-Experts Models with Compressed Experts
by: He, Yifei, et al.
Published: (2025)

Sparsity and Superposition in Mixture of Experts
by: Chaudhari, Marmik, et al.
Published: (2025)

Mixture of Diverse Size Experts
by: Sun, Manxi, et al.
Published: (2024)

Mixture of Concept Bottleneck Experts
by: De Santis, Francesco, et al.
Published: (2026)

Mixture of A Million Experts
by: He, Xu Owen
Published: (2024)

L-MoE: End-to-End Training of a Lightweight Mixture of Low-Rank Adaptation Experts
by: Ji, Shihao, et al.
Published: (2025)

Towards Efficient Federated Learning of Networked Mixture-of-Experts for Mobile Edge Computing
by: Gao, Song, et al.
Published: (2025)

HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts
by: Fang, Zihan, et al.
Published: (2026)

AnyExperts: On-Demand Expert Allocation for Multimodal Language Models with Mixture of Expert
by: Gao, Yuting, et al.
Published: (2025)

Accelerating Mixture-of-Expert Inference with Adaptive Expert Split Mechanism
by: Yan, Jiaming, et al.
Published: (2025)

MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
by: Wu, Han, et al.
Published: (2025)

Mixture of Experts in Large Language Models
by: Zhang, Danyang, et al.
Published: (2025)

Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning
by: Zhao, Ziyu, et al.
Published: (2025)

Graph Knowledge Distillation to Mixture of Experts
by: Rumiantsev, Pavel, et al.
Published: (2024)

Mixture of Weak & Strong Experts on Graphs
by: Zeng, Hanqing, et al.
Published: (2023)

Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning
by: Yu, En, et al.
Published: (2025)

Unified Class and Domain Incremental Learning with Mixture of Experts for Indoor Localization
by: Singampalli, Akhil, et al.
Published: (2025)

Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning
by: Zhao, Ziyu, et al.
Published: (2024)

Modeling Expert Interactions in Sparse Mixture of Experts via Graph Structures
by: Nguyen-Nhat, Minh-Khoi, et al.
Published: (2025)

Exploring Expert Specialization through Unsupervised Training in Sparse Mixture of Experts
by: Nikolic, Strahinja, et al.
Published: (2025)

UniPool: A Globally Shared Expert Pool for Mixture-of-Experts
by: Huang, Minbin, et al.
Published: (2026)