Fedopt: Towards communication efficiency and privacy preservation in federated learning
Artificial Intelligence (AI) has been applied to solve various challenges of real-world
problems in recent years. However, the emergence of new AI technologies has brought …
problems in recent years. However, the emergence of new AI technologies has brought …
[PDF][PDF] Understanding clipping for federated learning: Convergence and client-level differential privacy
Providing privacy protection has been one of the primary motivations of Federated Learning
(FL). Recently, there has been a line of work on incorporating the formal privacy notion of …
(FL). Recently, there has been a line of work on incorporating the formal privacy notion of …
LDP-FL: Practical private aggregation in federated learning with local differential privacy
Train machine learning models on sensitive user data has raised increasing privacy
concerns in many areas. Federated learning is a popular approach for privacy protection …
concerns in many areas. Federated learning is a popular approach for privacy protection …
Fedclip: Fast generalization and personalization for clip in federated learning
Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …
Federated learning on heterogeneous and long-tailed data via classifier re-training with federated features
Federated learning (FL) provides a privacy-preserving solution for distributed machine
learning tasks. One challenging problem that severely damages the performance of FL …
learning tasks. One challenging problem that severely damages the performance of FL …
Ensemble attention distillation for privacy-preserving federated learning
We consider the problem of Federated Learning (FL) where numerous decentralized
computational nodes collaborate with each other to train a centralized machine learning …
computational nodes collaborate with each other to train a centralized machine learning …
Federated model distillation with noise-free differential privacy
Conventional federated learning directly averages model weights, which is only possible for
collaboration between models with homogeneous architectures. Sharing prediction instead …
collaboration between models with homogeneous architectures. Sharing prediction instead …
Federated learning with sparsification-amplified privacy and adaptive optimization
Federated learning (FL) enables distributed agents to collaboratively learn a centralized
model without sharing their raw data with each other. However, data locality does not …
model without sharing their raw data with each other. However, data locality does not …
Preserving privacy in federated learning with ensemble cross-domain knowledge distillation
Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively
train a central model while the training data remains decentralized. Existing FL methods …
train a central model while the training data remains decentralized. Existing FL methods …
Federated learning with differential privacy: Algorithms and performance analysis
Federated learning (FL), as a type of distributed machine learning, is capable of significantly
preserving clients' private data from being exposed to adversaries. Nevertheless, private …
preserving clients' private data from being exposed to adversaries. Nevertheless, private …
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