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Found 31 papers, 4 papers with code
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
For better model personalization, we point out that the hard-won personalized models are not well exploited and propose "inherited private model" to store the personalization experience.
ECLM: Efficient Edge-Cloud Collaborative Learning with Continuous Environment Adaptation
In this paper, we propose ECLM, an edge-cloud collaborative learning framework for rapid model adaptation for dynamic edge environments.
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.
GFlowNets with Human Feedback
We propose the GFlowNets with Human Feedback (GFlowHF) framework to improve the exploration ability when training AI models.
Generalized Universal Domain Adaptation with Generative Flow Networks
We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories.
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs
Many score-based active learning methods have been successfully applied to graph-structured data, aiming to reduce the number of labels and achieve better performance of graph neural networks based on predefined score functions.
Multi-agent Policy Reciprocity with Theoretical Guarantee
We then define an adjacency space for mismatched states and design a plug-and-play module for value iteration, which enables agents to infer more precise returns.
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
data, we propose a clustered Bayesian FL model named cFedbayes by learning different prior distributions for different clients.
GFlowCausal: Generative Flow Networks for Causal Discovery
Causal discovery aims to uncover causal structure among a set of variables.
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
To make practical, we further devise an offline RL algorithm to approximately find the solution.
Ranked #1 on
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on D4RL
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Complex teachers tend to be over-confident and traditional temperature scaling limits the efficacy of {\it class discriminability}, resulting in less discriminative wrong class probabilities.
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies
Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize policy performance.
To Store or Not? Online Data Selection for Federated Learning with Limited Storage
We first define a new data valuation metric for data evaluation and selection in FL with theoretical guarantees for speeding up model convergence and enhancing final model accuracy, simultaneously.
Tensor Decomposition based Personalized Federated Learning
Federated learning (FL) is a new distributed machine learning framework that can achieve reliably collaborative training without collecting users' private data.
DP$^2$-VAE: Differentially Private Pre-trained Variational Autoencoders
Similar to other differentially private (DP) learners, the major challenge for DPGM is also how to achieve a subtle balance between utility and privacy.
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?
To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+2
Avoid Overfitting User Specific Information in Federated Keyword Spotting
Federated KWS (FedKWS) could serve as a solution without directly sharing users' data.
Personalized Federated Learning via Variational Bayesian Inference
Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients.
Federated Learning with Position-Aware Neurons
The permutation invariance property of neural networks and the non-i. i. d.
Sparse Federated Learning with Hierarchical Personalized Models
To this end, we propose a personalized FL algorithm using a hierarchical proximal mapping based on the moreau envelop, named sparse federated learning with hierarchical personalized models (sFedHP), which significantly improves the global model performance facing diverse data.
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.
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning
In this paper, we develop a vertical-horizontal federated learning (VHFL) process, where the global feature is shared with the agents in a procedure similar to that of vertical FL without any extra communication rounds.
Unified Group Fairness on Federated Learning
We validate the advantages of the FMDA-M algorithm with various kinds of distribution shift settings in experiments, and the results show that FMDA-M algorithm outperforms the existing fair FL algorithms on unified group fairness.
Preliminary Steps Towards Federated Sentiment Classification
Automatically mining sentiment tendency contained in natural language is a fundamental research to some artificial intelligent applications, where solutions alternate with challenges.
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
We advocate the proposed methods could serve as a preliminary try to explore where to privatize for a novel non-iid scene.
Domain Adaptation without Model Transferring
Existing unsupervised domain adaptation (UDA) methods can achieve promising performance without transferring data from source domain to target domain.
Sparse Personalized Federated Learning
By incorporating an approximated L1-norm and the correlation between client models and global model into standard FL loss function, the performance on statistical diversity data is improved and the communicational and computational loads required in the network are reduced compared with non-sparse FL.
Structured Directional Pruning via Perturbation Orthogonal Projection
Structured pruning is an effective compression technique to reduce the computation of neural networks, which is usually achieved by adding perturbations to reduce network parameters at the cost of slightly increasing training loss.
Convergence Analysis and System Design for Federated Learning over Wireless Networks
Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets.
Exploring Uncertainty in Deep Learning for Construction of Prediction Intervals
In this paper, We explore the uncertainty in deep learning to construct the prediction intervals.
Loosely Coupled Federated Learning Over Generative Models
This paper proposes Loosely Coupled Federated Learning (LC-FL), a framework using generative models as transmission media to achieve low communication cost and heterogeneous federated learning.
