4 min readGuide
Privacy, Secure Aggregation and Robustness in DFL
Combine differential privacy, secure aggregation and robust DFL with explicit trust assumptions, local threat budgets and joint evaluation.
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.
18 articles. Page 1 of 3.
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.
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
Modalis: Prototype Exchange for Heterogeneous Multimodal DFL
How Modalis works: multimodal prototypes, gating and alignment in DFL. Published results, limitations and proposed ablations.
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.