REVIEW 10 cited by
Fair Resource Allocation in Federated Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Fair Resource Allocation in Federated Learning
read the original abstract
Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by fair resource allocation in wireless networks that encourages a more fair (specifically, a more uniform) accuracy distribution across devices in federated networks. To solve q-FFL, we devise a communication-efficient method, q-FedAvg, that is suited to federated networks. We validate both the effectiveness of q-FFL and the efficiency of q-FedAvg on a suite of federated datasets with both convex and non-convex models, and show that q-FFL (along with q-FedAvg) outperforms existing baselines in terms of the resulting fairness, flexibility, and efficiency.
Forward citations
Cited by 10 Pith papers
-
Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
The FedSurg challenge benchmarks federated learning on appendectomy videos and finds only 26% F1 on unseen centers even with centralized data, plus extra penalties from decentralization, with spatiotemporal models per...
-
Federated Cross-Modal Retrieval with Missing Modalities via Semantic Routing and Adapter Personalization
RCSR is a personalization-friendly federated framework that improves cross-modal retrieval accuracy and stability under missing modalities via semantic routing and adapters.
-
DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning
DFedReweighting is a unified reweighting method for decentralized federated learning that customizes aggregation via target metrics and strategies to improve fairness, Byzantine robustness, and other objectives while ...
-
Adaptive Federated Optimization
Proposes federated adaptive optimizers (FedAdagrad, FedAdam, FedYogi) with convergence analysis for non-convex objectives under data heterogeneity and reports empirical gains over FedAvg.
-
FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching
FedSteer constructs a gradient subspace from cached client updates, projects active gradients to obtain coordinates, and reuses those coordinates on the drifted subspace to correct extreme staleness in federated learning.
-
QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
QSplitFL is a DQN framework that selects split points in split federated learning from hardware metrics with a decayed loss-drop reward and committee voting, reporting faster convergence and higher accuracy than basel...
-
HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning
HASA computes client heterogeneity scores from local data and assigns wider subnets to less heterogeneous clients, raising mean client test accuracy from 13.82% to 14.32% and improving worst-client accuracy versus uni...
-
FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility
FedPF frames privacy and fairness in federated learning as a zero-sum game, shows privacy reduces bias-detection power under finite samples, and cuts discrimination up to 42.9% while retaining competitive accuracy.
-
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
FedBB addresses inter-case, inter-class, and inter-client imbalances in federated learning via Positive Negative Balanced loss and Client Balanced Reweighting, outperforming baselines on X-ray and natural image datase...
-
RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility
RESFL integrates adversarial feature disentanglement and uncertainty-aware client weighting in federated learning to reduce membership inference attacks and equality-of-opportunity gaps while preserving model utility ...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.