The field of federated learning is rapidly advancing, with a focus on developing innovative solutions for IoT and edge computing applications. Recent research has explored the use of federated learning for IoT data analytics, falling people detection, and smart prediction and recommendation applications. These solutions aim to provide efficient, flexible, and extensible data analytics while protecting the privacy of exchanged data. Noteworthy papers in this area include FedMicro-IDA, which proposes a federated learning and microservices-based framework for IoT data analytics, and EPFL, which presents an ensembled penalized federated learning framework for falling people detection. Another significant contribution is DictPFL, which achieves full gradient protection with minimal overhead, making it a practical solution for real-world deployment.
Advances in Federated Learning for IoT and Edge Computing
Sources
FedMicro-IDA: A Federated Learning and Microservices-based Framework for IoT Data Analytics
An Ensembled Penalized Federated Learning Framework for Falling People Detection
DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
Generative Federated Learning for Smart Prediction and Recommendation Applications
Sensor-Specific Transformer (PatchTST) Ensembles with Test-Matched Augmentation
The Qey: Implementation and performance study of post quantum cryptography in FIDO2
Benchmarking Catastrophic Forgetting Mitigation Methods in Federated Time Series Forecasting
Toward provably private analytics and insights into GenAI use
Privacy-preserving Decision-focused Learning for Multi-energy Systems
Power to the Clients: Federated Learning in a Dictatorship Setting
Privacy-Aware Federated nnU-Net for ECG Page Digitization
Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data
QuantumShield: Multilayer Fortification for Quantum Federated Learning
Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks
SGFusion: Stochastic Geographic Gradient Fusion in Federated Learning
A Privacy-Preserving Ecosystem for Developing Machine Learning Algorithms Using Patient Data: Insights from the TUM.ai Makeathon
Lightweight Federated Learning in Mobile Edge Computing with Statistical and Device Heterogeneity Awareness
Prompt Estimation from Prototypes for Federated Prompt Tuning of Vision Transformers
A Zero Added Loss Multiplexing (ZALM) Source Simulation
UnifiedFL: A Dynamic Unified Learning Framework for Equitable Federation