ReviewSensors (Basel, Switzerland)2022
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State-of-the-Art and Future Directions.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Cloud assisted blockchain-enabled split federated learning framework for security and privacy-preserving of IoMT in healthcare 5.0.Scientific reports · 2026Article
- Federated Learning in Edge Computing: Vulnerabilities, Attacks, and Defenses-A Survey.Sensors (Basel, Switzerland) · 2026Review
- U-SplitDoRA: an improved privacy-preserved U-shaped split parameter-efficient fine-tuning framework through weight decomposition for large language models.Frontiers in artificial intelligence · 2026Article
- Analyzing the vulnerabilities in Split Federated Learning: assessing the robustness against data poisoning attacks.Scientific reports · 2025Article
- Personalized Fair Split Learning for Resource-Constrained Internet of Things.Sensors (Basel, Switzerland) · 2023Article
- Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each have unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, the combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with the hope of arousing the research community's interest in this emerging field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.