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Found 21 papers, 16 papers with code
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued.
Exploiting Label Skews in Federated Learning with Model Concatenation
However, non-IID data has been a key challenge in FL, which could significantly degrade the accuracy of the final model.
Effective and Efficient Federated Tree Learning on Hybrid Data
To address this, we propose HybridTree, a novel federated learning approach that enables federated tree learning on hybrid data.
OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams
Thus, a natural question is how those open environment challenges look like and how existing incremental learning algorithms perform on real-world relational data streams.
SoK: Privacy-Preserving Data Synthesis
As the prevalence of data analysis grows, safeguarding data privacy has become a paramount concern.
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives
To fully unleash the potential of FL, we advocate rethinking the design of current FL frameworks and extending it to a more generalized concept: Open Federated Learning Platforms, positioned as a crowdsourcing collaborative machine learning infrastructure for all Internet users.
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
We then provide two robustness certification criteria: certified prediction and certified attack inefficacy for DPFL on both user and instance levels.
Practical Vertical Federated Learning with Unsupervised Representation Learning
As societal concerns on data privacy recently increase, we have witnessed data silos among multiple parties in various applications.
UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks
The platform streamlines the end-to-end workflow for distributed experimentation and deployment, encompassing 11 popular open-source FL frameworks.
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
To address these challenges, in this paper, we introduce a VFL framework with multiple heads (VIM), which takes the separate contribution of each client into account, and enables an efficient decomposition of the VFL optimization objective to sub-objectives that can be iteratively tackled by the server and the clients on their own.
Adversarial Collaborative Learning on Non-IID Features
Federated learning has been a popular approach to enable collaborative learning on multiple parties without exchanging raw data.
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
However, most existing studies in VFL disregard the "record linkage" process.
Model-Contrastive Federated Learning
A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties.
Federated Learning on Non-IID Data Silos: An Experimental Study
We find that non-IID does bring significant challenges in learning accuracy of FL algorithms, and none of the existing state-of-the-art FL algorithms outperforms others in all cases.
Practical One-Shot Federated Learning for Cross-Silo Setting
Federated learning enables multiple parties to collaboratively learn a model without exchanging their data.
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer
In this paper, we propose a novel federated learning algorithm FedKT that needs only a single communication round (i. e., round-optimal).
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems.
Privacy-Preserving Gradient Boosting Decision Trees
Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds.
Practical Federated Gradient Boosting Decision Trees
There have been several recent studies on how to train GBDTs in the federated learning setting.
Adaptive Kernel Value Caching for SVM Training
Our experimental results show that EFU often has 20\% higher hit ratio than LRU in the training with the Gaussian kernel.
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.
