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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.

169,181 papers · 148 categories

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7 results for “PFL”

Counterparty Trading Limits Revisited:CSAs, IM, SwapAgent(r), from PFE to PFLq-fin.RM

New PFL method addresses counterparty risk challenges.

problem Challenges with PFE for counterparty limits.

method Introducing PFL combining ES and LGD, and variants aPFL and paPFL.

result PFL and variants provide a better measure of counterparty risk.

counterparty risk PFE PFL Initial Margin collateralization

2017-10-09abs ↗pdf ↗

Clust-PSI-PFL: A Population Stability Index Approach for Clustered Non-IID Personalized Federated Learningcs.LG

Clust-PSI-PFL uses PSI to improve accuracy and fairness in federated learning.

problem Non-IID data biases federated learning performance.

method Clust-PSI-PFL uses clustering and PSI to form homogeneous groups of clients.

result Clust-PSI-PFL delivers up to 18% higher global accuracy and improves client fairness.

federated learning non-IID data population stability index personalized federated learning cluster-based approach

2025-12-23abs ↗pdf ↗

Federated Ensemble Learning with Progressive Model Personalizationstat.ML

Boosting-inspired framework for federated learning with progressive model personalization.

problem Statistical heterogeneity across clients in federated learning.

method Construct an ensemble of personalized models, progressively increasing the depth of the personalized component while controlling its effective complexity.

result Consistently outperforms state-of-the-art PFL methods under heterogeneous data distributions.

Federated Learning Personalized Federated Learning Boosting Progressive Model Personalization Generalization Bounds

2026-02-15abs ↗pdf ↗

Personalized Federated Learning with Gaussian Processescs.LG

pFedGP uses Gaussian processes for personalized federated learning with improved kernel learning.

problem Learning personalized models across clients with limited data.

method pFedGP uses Gaussian processes with deep kernel learning, including a shared neural network kernel and inducing points.

result pFedGP achieves well-calibrated predictions and significantly outperforms baseline methods.

federated learning personalized models Gaussian processes kernel learning inducing points

2021-06-29abs ↗pdf ↗

Hierarchical Over-the-Air FedGradNormcs.LG

Proposes HOTA-FedGradNorm for faster and robust PFL in noisy channels.

problem Statistical heterogeneity across clients in federated learning.

method Dynamic weighting, OTA aggregation, hierarchical federated learning.

result Training speed improvement and robustness against channel effects.

federated learning dynamic weighting OTA aggregation hierarchical learning robustness

2022-12-14abs ↗pdf ↗

Personalized Federated Learning with Attention-based Client Selectioncs.LG

FedACS uses attention to select clients with similar data for federated learning.

problem Non-IID data and data scarcity in federated learning.

method FedACS integrates an attention mechanism to prioritize clients with similar data distributions.

result FedACS improves federated learning performance by addressing non-IID data and data scarcity.

federated learning attention mechanism client selection non-IID data data scarcity

2023-12-23abs ↗pdf ↗

Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guaranteesstat.ML

This paper analyzes the trade-off between accuracy and communication in personalized federated learning.

problem The accuracy-communication trade-off in personalized federated learning.

method The paper provides a quantitative characterization of the personalization degree on the trade-off, establishing minimax optimality.

result The paper offers theoretical insights for choosing the personalization degree and validates the results on synthetic and real-world datasets.

federated learning personalized learning statistical accuracy communication cost minimax optimality

2024-10-11abs ↗pdf ↗