arXiv (All)
open access
Asking the Crowd the Right Question: Bias-Cancelling Weights for Federated Learning
A federated objective is a weighted sum of client risks, and the weights are almost always fixed in advance. We treat them instead as the only instrument of a wisdom-of-crowds mechanism: clients are noisy views of one truth, each seeing it through an independent distortion that is unbiased across th
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata#414034
- Enhanced Security and Privacy via Fragmented Federated Learning#400577
- Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems#1014283
- FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors#1318001
- Influence-Oriented Personalized Federated Learning#987386
Record · ID 1200322
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive