Difference between revisions of "Model Heterogeneity in Federated Learning"
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|Summary=Group users within a federated learning environment into different learning overlays according to their behavioural similarities | |Summary=Group users within a federated learning environment into different learning overlays according to their behavioural similarities | ||
|Keywords=Federated Learning, Clustering | |Keywords=Federated Learning, Clustering | ||
| + | |TimeFrame=Fall 2022 | ||
| + | |References=Advances and Open Problems in Federated Learning: | ||
| + | https://hal.inria.fr/hal-02406503/document | ||
| + | |||
| + | FedML: A Research Library and Benchmark for Federated Machine Learning: https://arxiv.org/pdf/2007.13518.pdf | ||
|Supervisor=Amira Soliman, Sławomir Nowaczyk, | |Supervisor=Amira Soliman, Sławomir Nowaczyk, | ||
|Level=Master | |Level=Master | ||
Latest revision as of 17:52, 17 September 2022
| Title | Model Heterogeneity in Federated Learning |
|---|---|
| Summary | Group users within a federated learning environment into different learning overlays according to their behavioural similarities |
| Keywords | Federated Learning, Clustering |
| TimeFrame | Fall 2022 |
| References | Advances and Open Problems in Federated Learning:
https://hal.inria.fr/hal-02406503/document FedML: A Research Library and Benchmark for Federated Machine Learning: https://arxiv.org/pdf/2007.13518.pdf |
| Prerequisites | |
| Author | |
| Supervisor | Amira Soliman, Sławomir Nowaczyk |
| Level | Master |
| Status | Open |
Federated Learning (FL) has been introduced as an alternative distributed and privacy-friendly learning approach. FL allows users to train models locally on their devices using their sensitive data, and communicate intermediate model updates to a central server without the need to centrally store the data. The principal advantage of FL is the decoupling of global model training from the need for direct access to the raw data. Accordingly, FL offers a solution to learn from private personal data such as biometrics, text input, and location coordinates where models can be trained for many services in a privacy-preserving manner.
Generating a single global model that accumulates all user behaviors might not produce the best model for particular categories of users. Specifically, the global averaging model enforces a bias towards the behavioral patterns provided by the majority, while suppressing the patterns of less significant users. Thus, it is interesting to provide overlay-based FL techniques that can group users in different learning overlays according to their behavioral similarities. The objective of this thesis is to introduce a mechanism for grouping users with similar behaviors and develop a hierarchical aggregation mechanism to provide more than one model, automatically identifying the best group for a given node.
