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Found 12 papers, 1 papers with code
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
We show that the generalization performance of a client can be improved only by collaborating with other clients that have more training data and similar data distribution.
Feature Matching Data Synthesis for Non-IID Federated Learning
For better privacy preservation, we propose a hard feature augmentation method to transfer real features towards the decision boundary, with which the synthetic data not only improve the model generalization but also erase the information of real features.
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Without data centralization, FL allows clients to share local information in a privacy-preserving manner.
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates need to be collected at a server.
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation.
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning
Federated learning (FL) attempts to train a global model by aggregating local models from distributed devices under the coordination of a central server.
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design
During training, the server computes gradients on the global coded dataset to compensate for the missing model updates of the straggling devices.
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Federated learning (FL) strives to enable collaborative training of machine learning models without centrally collecting clients' private data.
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees
Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework, where many clients collaboratively train a machine learning model by exchanging model updates with a parameter server instead of sharing their raw data.
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity
By exploiting the low-latency communication among edge servers for efficient model sharing, SD-FEEL can incorporate more training data, while enjoying much lower latency compared with conventional federated learning.
Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients
Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks.
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large communication latency in Cloud-based machine learning solutions, while preserving data privacy.
