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Found 17 papers, 5 papers with code
Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering
In the online phase, FOCUS dynamically adapts these patterns to the current input and captures dependencies between the input segment and high-level events, enabling both accurate and efficient forecasting.
Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach
Furthermore, existing solutions fail to achieve high accuracy and efficiency, making practical use of SV still out of reach, because they ignore choosing suitable computation scheme for approximation framework and overlook the property of utility function in FL.
FineFilter: A Fine-grained Noise Filtering Mechanism for Retrieval-Augmented Large Language Models
We optimize each module to tackle complex reasoning challenges: (1) Clue extractor firstly uses sentences containing the answer and similar ones as fine-tuned targets, aiming at extracting sufficient potential clues; (2) Re-ranker is trained to prioritize effective clues based on the real feedback from generation module, with clues capable of generating correct answer as positive samples and others as negative; (3) Truncator takes the minimum clues needed to answer the question (truncation point) as fine-tuned targets, and performs truncation on the re-ranked clues to achieve fine-grained noise filtering.
Ten Challenging Problems in Federated Foundation Models
This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency.
Modeling Inter-Intra Heterogeneity for Graph Federated Learning
To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH).
eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules
Link prediction (LP) is crucial for Knowledge Graphs (KG) completion but commonly suffers from interpretability issues.
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning
This vulnerability may lead the current heuristic hyperparameter configuration of SecureBoost to a suboptimal trade-off between utility, privacy, and efficiency, which are pivotal elements toward a trustworthy federated learning system.
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems.
SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning
To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage.
A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management
In this paper, we propose RegionGen, a data-driven region generation framework that can specify regions with key characteristics (e. g., good spatial semantic meaning and predictability) by modeling region generation as a multi-objective optimization problem.
p-Meta: Towards On-device Deep Model Adaptation
Data collected by IoT devices are often private and have a large diversity across users.
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing
Ride hailing is a widespread shared mobility application where the central issue is to assign taxi requests to drivers with various objectives.
Transferring Knowledge Distillation for Multilingual Social Event Detection
Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.
An Efficient Approach for Cross-Silo Federated Learning to Rank
Traditional learning-to-rank (LTR) models are usually trained in a centralized approach based upon a large amount of data.
Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms
In this paper we propose a unified value-based dynamic learning framework (V1D3) for tackling both tasks.
Pruning-Aware Merging for Efficient Multitask Inference
Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost.
Federated Machine Learning: Concept and Applications
We propose a possible solution to these challenges: secure federated learning.
