SWB-DM: A Calibrated Sliced-Wasserstein-Barycenter Aggregator with Delayed-Momentum Caching for Byzantine-Robust Federated Learning under Partial Participation
This story is from 2026-09-16. It is preserved in the archive; the latest stories are on the live feed.
arXiv:2609.16099v1 Announce Type: new Abstract: Robust aggregation methods for federated learning quietly rest on a fragile assumption: that whoever shows up in a given round is a fair sample of the full population. In practice, they rarely are. When only a handful of clients participate per round,…
Read the full story at arXiv cs.LG ↗
Timeline · 1 report
- 2026-09-16 04:00 · arXiv cs.LG
SWB-DM: A Calibrated Sliced-Wasserstein-Barycenter Aggregator with Delayed-Momentum Caching for Byzantine-Robust Federated Learning under Partial Participation
More stories
- AI's role in building AI surging? Anthropic says Claude now leads 26% of its R&D — Mint AI
- Google Joins OpenAI, Anthropic, Meta in Disclosing AI Hacks — Bloomberg AI
- Introducing Kimi K3 on Amazon Bedrock — AWS Machine Learning Blog
- Optimizing agent system prompts with Amazon Bedrock AgentCore — AWS Machine Learning Blog
- Introducing Amazon SageMaker HyperPod Inference Gateway — AWS Machine Learning Blog
- Introducing Astra for Law — OpenAI News
- Novo Nordisk Will Use Anthropic’s Claude for Drug Research — Wall Street Journal Technology
- Gemini Hacked Three Companies in First Known Breakout by Google’s AI — Wall Street Journal Technology
Get the daily brief of stories like this at 6:30 every morning →