5 min readResearch note
Robust Decentralized Federated Learning: Limits and Open Questions
Open questions in robust DFL: dynamic graphs, manipulated context, privacy, missing modalities and experiments to test the limits.
Research Notes on Federated Learning, Cybersecurity and Cyberdefense
Research notes and technical guides on federated learning, distributed AI, cyberdefense, trustworthy systems, and privacy-preserving platforms.
Topic: Decentralized Federated Learning Clear topic
5 articles. Page 1 of 1.
3 min readResearch note
D-MoE: Distributed Mixture of Experts for Edge LLMs
Distributed MoE at the edge: expert placement, critical-path latency, bandwidth, activations and failure recovery.
5 min readResearch note
Communication Efficiency in DFL: Why Prototypes Matter
Compare models and prototypes in DFL: message size, cumulative bytes, quantization and target quality under network constraints.
5 min readResearch note
When Federated Clients See Different Modalities
Multimodal federated learning with non-IID data and missing sensors: masks, prototypes and evaluation by modality and client.
5 min readResearch note
DFL Is More Than Federated Learning Without a Server
Why DFL changes coordination, consensus and trust. Identify central dependencies and test which services survive a failure.