Proof pending. Core topic summary fields are still materializing.
Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, clients tend to have non-Independent and Identically Distributed (non-IID) data, whic...
Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data. In practice, however, a discrepancy between the factor-wise averaging used to p...
Synchronous federated learning scales poorly due to the straggler effect. Asynchronous algorithms increase the update throughput by processing updates upon arrival, but they introduce two fundamental ...
In this paper, we present Federated Robust Curvature Optimization (FedRCO), a novel second-order optimization framework designed to improve convergence speed and reduce communication cost in Federated...
It is commonly believed that gradient compression in federated learning (FL) enjoys significant improvement in communication efficiency with negligible performance degradation. In this paper, we find ...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID federated-learning-optimization | Route /topic/federated-learning-optimization
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/federated-learning-optimizationMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Federated Learning Optimization",
"cluster": "Federated Learning Optimization"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Federated Learning Optimization",
"normalized_query": "federated-learning-optimization",
"route": "/topic/federated-learning-optimization",
"paper_ref": null,
"topic_slug": "federated-learning-optimization",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.