FT-MoE: Sustainable-learning Mixture of Experts for Fault-Tolerant Computing
Fuente:
arXiv
Saved in:
| Main Authors: | Xiao, Wenjing, Song, Wenhao, Chen, Miaojiang, Chen, Min |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
by: Zhang, Boyang, et al.
Published: (2025)
by: Zhang, Boyang, et al.
Published: (2025)
MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
by: Chen, Xiaodong, et al.
Published: (2025)
by: Chen, Xiaodong, et al.
Published: (2025)
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)
by: Jin, Peng, et al.
Published: (2024)
Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026)
by: Polson, Nick, et al.
Published: (2026)
GraphEdge: Dynamic Graph Partition and Task Scheduling for GNNs Computing in Edge Network
by: Xiao, Wenjing, et al.
Published: (2025)
by: Xiao, Wenjing, et al.
Published: (2025)
$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
by: Takashiro, Shota, et al.
Published: (2026)
by: Takashiro, Shota, et al.
Published: (2026)
VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting
by: Chen, Hao, et al.
Published: (2024)
by: Chen, Hao, et al.
Published: (2024)
DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models
by: Aghdam, Maryam Akhavan, et al.
Published: (2024)
by: Aghdam, Maryam Akhavan, et al.
Published: (2024)
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
by: Chen, Yuanteng, et al.
Published: (2025)
by: Chen, Yuanteng, et al.
Published: (2025)
Mixture of Experts (MoE): A Big Data Perspective
by: Gan, Wensheng, et al.
Published: (2025)
by: Gan, Wensheng, et al.
Published: (2025)
SDG-MoE: Signed Debate Graph Mixture-of-Experts
by: Kulibaba, Stepan, et al.
Published: (2026)
by: Kulibaba, Stepan, et al.
Published: (2026)
$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts
by: Chen, Guanjie, et al.
Published: (2024)
by: Chen, Guanjie, et al.
Published: (2024)
SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts
by: Muzio, Alexandre, et al.
Published: (2024)
by: Muzio, Alexandre, et al.
Published: (2024)
Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
MoE Pathfinder: Trajectory-driven Expert Pruning
by: Yang, Xican, et al.
Published: (2025)
by: Yang, Xican, et al.
Published: (2025)
Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
by: Li, Lujun, et al.
Published: (2025)
by: Li, Lujun, et al.
Published: (2025)
Anchor-MoE: A Mean-Anchored Mixture of Experts For Probabilistic Regression
by: Su, Baozhuo, et al.
Published: (2025)
by: Su, Baozhuo, et al.
Published: (2025)
HI-MoE: Hierarchical Instance-Conditioned Mixture-of-Experts for Object Detection
by: Vashkelis, Vadim, et al.
Published: (2026)
by: Vashkelis, Vadim, et al.
Published: (2026)
PWC-MoE: Privacy-Aware Wireless Collaborative Mixture of Experts
by: Su, Yang, et al.
Published: (2025)
by: Su, Yang, et al.
Published: (2025)
MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing
by: Ma, Haiyue, et al.
Published: (2025)
by: Ma, Haiyue, et al.
Published: (2025)
LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
by: Liu, Xinyi, et al.
Published: (2026)
by: Liu, Xinyi, et al.
Published: (2026)
Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
by: Yun, Sukwon, et al.
Published: (2024)
by: Yun, Sukwon, et al.
Published: (2024)
L-MoE: End-to-End Training of a Lightweight Mixture of Low-Rank Adaptation Experts
by: Ji, Shihao, et al.
Published: (2025)
by: Ji, Shihao, et al.
Published: (2025)
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
by: Liu, Xu, et al.
Published: (2024)
by: Liu, Xu, et al.
Published: (2024)
I2MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts
by: Xin, Jiayi, et al.
Published: (2025)
by: Xin, Jiayi, et al.
Published: (2025)
MoC-System: Efficient Fault Tolerance for Sparse Mixture-of-Experts Model Training
by: Cai, Weilin, et al.
Published: (2024)
by: Cai, Weilin, et al.
Published: (2024)
MP-MoE: Matrix Profile-Guided Mixture of Experts for Precipitation Forecasting
by: Tran, Huyen Ngoc, et al.
Published: (2026)
by: Tran, Huyen Ngoc, et al.
Published: (2026)
BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification
by: Zhang, Jing, et al.
Published: (2025)
by: Zhang, Jing, et al.
Published: (2025)
RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
by: Zhong, Zhengjia, et al.
Published: (2026)
by: Zhong, Zhengjia, et al.
Published: (2026)
PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs
by: Zhang, Ze Yu, et al.
Published: (2025)
by: Zhang, Ze Yu, et al.
Published: (2025)
Alloc-MoE: Budget-Aware Expert Activation Allocation for Efficient Mixture-of-Experts Inference
by: Liu, Baihui, et al.
Published: (2026)
by: Liu, Baihui, et al.
Published: (2026)
MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction
by: Wang, Xiaoyang, et al.
Published: (2025)
by: Wang, Xiaoyang, et al.
Published: (2025)
MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition
by: Yang, Cheng, et al.
Published: (2024)
by: Yang, Cheng, et al.
Published: (2024)
MoE-DisCo:Low Economy Cost Training Mixture-of-Experts Models
by: Ye, Xin, et al.
Published: (2026)
by: Ye, Xin, et al.
Published: (2026)
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
by: Shi, Xiaoming, et al.
Published: (2024)
by: Shi, Xiaoming, et al.
Published: (2024)
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
by: Ludziejewski, Jan, et al.
Published: (2025)
by: Ludziejewski, Jan, et al.
Published: (2025)
$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts
by: Pióro, Maciej, et al.
Published: (2024)
by: Pióro, Maciej, et al.
Published: (2024)
MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression
by: Sun, Libo, et al.
Published: (2026)
by: Sun, Libo, et al.
Published: (2026)
MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models
by: Chitty-Venkata, Krishna Teja, et al.
Published: (2025)
by: Chitty-Venkata, Krishna Teja, et al.
Published: (2025)
Similar Items
-
FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
by: Zhang, Boyang, et al.
Published: (2025) -
MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
by: Chen, Xiaodong, et al.
Published: (2025) -
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024) -
Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026) -
GraphEdge: Dynamic Graph Partition and Task Scheduling for GNNs Computing in Edge Network
by: Xiao, Wenjing, et al.
Published: (2025)