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Found 14 papers, 4 papers with code
Improving Group Connectivity for Generalization of Federated Deep Learning
Bridging the gap between LMC and FL, in this paper, we leverage fixed anchor models to empirically and theoretically study the transitivity property of connectivity from two models (LMC) to a group of models (model fusion in FL).
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks.
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields.
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion
In deep learning, stochastic gradient descent often yields functionally similar yet widely scattered solutions in the weight space even under the same initialization, causing barriers in the Linear Mode Connectivity (LMC) landscape.
Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers
Therefore, there is a need to develop a scalable framework for geometric fracture assembly without relying on semantic information.
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning
A typical way of pFL focuses on label distribution skew, and they adopt a decoupling scheme where the model is split into a common feature extractor and two prediction heads (generic and personalized).
Understanding Prompt Tuning for V-L Models Through the Lens of Neural Collapse
It is found that NC optimality of text-to-image representations shows a positive correlation with downstream generalizability, which is more severe under class imbalance settings.
Universal Domain Adaptation via Compressive Attention Matching
To address this issue, we propose a Universal Attention Matching (UniAM) framework by exploiting the self-attention mechanism in vision transformer to capture the crucial object information.
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Edge-cloud Collaborative Learning with Federated and Centralized Features
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy.
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
It includes a noise-resilient local solver and a robust global aggregator.
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier
Recent advances in neural collapse have shown that the classifiers and feature prototypes under perfect training scenarios collapse into an optimal structure called simplex equiangular tight frame (ETF).
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
In federated learning (FL), weighted aggregation of local models is conducted to generate a global model, and the aggregation weights are normalized (the sum of weights is 1) and proportional to the local data sizes.
Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching
In the literature, centralized clustered FL algorithms require the assumption of the number of clusters and hence are not effective enough to explore the latent relationships among clients.
Ensemble Federated Adversarial Training with Non-IID data
Despite federated learning endows distributed clients with a cooperative training mode under the premise of protecting data privacy and security, the clients are still vulnerable when encountering adversarial samples due to the lack of robustness.
