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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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3 results for “FedNova”

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcs.LG

This paper tackles objective inconsistency in federated optimization with heterogeneous clients.

problem Objective inconsistency due to heterogeneity in clients' datasets and computation speeds.

method General framework for analyzing federated heterogeneous optimization algorithms, including FedAvg and FedProx, and proposing FedNova.

result FedNova eliminates objective inconsistency while preserving fast error convergence.

federated optimization heterogeneous clients objective inconsistency FedNova FedAvg

2020-07-15abs ↗pdf ↗

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computingcs.LG

FedHDPrivacy uses DP to improve FL in IoT, maintaining high accuracy.

problem Privacy threats in FL, especially in IoT environments.

method Integrates DP with neuro-symbolic computing, actively monitoring and adjusting noise.

result Maintains high performance in manufacturing monitoring, surpassing other FL methods.

Federated Learning Differential Privacy Hyperdimensional Computing IoT Machine Learning

2024-11-02abs ↗pdf ↗

FedShuffle: Recipes for Better Use of Local Work in Federated Learningcs.LG

FedShuffle improves local updates in FL, especially with data imbalance.

problem Data imbalance in FL leads to different clients performing different numbers of local updates.

method FedShuffle incorporates random reshuffling, data imbalance, and client sampling.

result FedShuffle improves upon FL methods that assume homogeneous updates in heterogeneous setups.

Federated Learning Local Updates Data Imbalance Random Reshuffling Client Sampling

2022-04-27abs ↗pdf ↗