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Found 12 papers, 4 papers with code
Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks
To address these challenges, we conduct a thorough theoretical convergence analysis for DFL and derive a convergence bound.
On Diversified Preferences of Large Language Model Alignment
Our analysis reveals a correlation between the calibration performance of reward models (RMs) and the alignment performance of LLMs.
Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization
To address these issues, we propose a novel method, i. e., Federated Latent Embedding Sharing Tensor factorization (FLEST), which is a novel approach using federated tensor factorization for KG completion.
Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients
Federated Learning (FL) has surged in prominence due to its capability of collaborative model training without direct data sharing.
Tackling Hybrid Heterogeneity on Federated Optimization via Gradient Diversity Maximization
The combined effects of statistical and system heterogeneity can significantly reduce the efficiency of federated optimization.
Enhanced Federated Optimization: Adaptive Unbiased Sampling with Reduced Variance
Central to this process is the technique of unbiased client sampling, which ensures a representative selection of clients.
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data.
FedNoisy: Federated Noisy Label Learning Benchmark
Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients.
Stochastic Clustered Federated Learning
Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices.
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society.
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning
However, privacy information could be leaked from uploaded gradients and be exposed to malicious attackers or an honest-but-curious server.
FedLab: A Flexible Federated Learning Framework
Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations.
