Privacy aware analytics at edge using federated learning
Privacy aware analytics at edge using Federated Learning In Cloud computing or centralized approach, data-sources or sensors collect data from environment; the data is then sent to a data center placed at a geographically different location to be processed and analyzed. This approach is not suitable for the applications required low latency and quick response time. Also, in massive machine type communications (MMTC) where thousands of sensors or IoT data sources collect and send data simultaneously, bandwidth becomes a bottleneck in data transmission. On contrary, Edge computing is a distributed computing paradigm which brings computation and data storage closer to the location where it is generated, to decrease latency and to save bandwidth. Edge analytics may not be a replacement for centralized data analytics, but both can supplement each other in delivering data insights. With the increasing demand and implementation of stringent data privacy laws and growing security concerns, the concept of Federated Learning (FL) has been introduced. In FL, edge applications can use their local device data to train an Machine Learning model required by the centralized server. The edge devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative and decentralized learning. In this talk, we will discuss about the decentralized learning approach in federated learning, implementing federated learning in edge devices, how it solves data localization, privacy and scalability issues, federated learning at edge and fog devices with practical use cases and open problems.
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Privacy aware analytics at edge using federated learning
- 1. Privacy aware analytics at edge using Federated Learning Arindam Banerjee Data Scientist, Ericsson 1Arindam Banerjee
- 2. Shaping the next decade… • 5G • Exponential growth of Smart devices • Exponential growth of data • Stricter data privacy and protection laws • Automation and Analytics 2Arindam Banerjee
- 3. 5G and IoT: Ushering in a new era • Foundation for realizing the full potential of IoT. • 550 million 5G subscriptions in 2022 - Ericsson AB’s Mobility Report. • A fertile ground for innovations and customers engagements. • Network slicing. Segmenting network: enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (URLLC), massive Machine Type 3Arindam Banerjee
- 4. Exponential growth of Data • Billions of IoT sensors and mobile devices at edge. • Uncertainty about network quality. • Intermittently available. • From massive data to context-aware, smarter apps. 4Arindam Banerjee
- 5. Privacy and Security • IoT data is growing fast but security remains a big hurdle. • Hyper-personalization comes with loss of privacy. • Laws and regulations: Health Insurance Portability and Accountability Act (HIPAA), General Data Protection 5Arindam Banerjee
- 6. Problems faced in CentralizedAnalytics Edge to cloud data transmission Bottleneck - Network bandwidth Security and privacy Infrastructure cost Underutilized resources at edge 6Arindam Banerjee
- 7. Federated Learning (FL) • Collaborative learning • Downloaded model learns from local data • Compressed update is sent back to the cloud • Federated averaging • New model is communicated back to edge • Improved local inference empowers personalization. 7Arindam Banerjee
- 8. Ref.: Ericsson Technology Review, 09-2019. 8Arindam Banerjee
- 9. Features of Federated Learning Edge IoT devices have limited network bandwidth. Devices are intermittently available for training. Device may choose not to participate in the training. Massive number of devices but inconsistent. Naturally arising non- IID partition. Massively parallel. 9Arindam Banerjee
- 10. Advantages of Federated Learning • Privacy: Data localization Data retention • Less data transfer - less Network Bandwidth • Better battery life. • Scalability. • Less infrastructure cost. • Low latency for inference. 10Arindam Banerjee
- 11. • High convergence time. • Unavailability of edge devices. • Irregular or missed updates. 11Arindam Banerjee
- 12. Model Poisoning Image Source: Clement Fung et al. – “Mitigating Sybils in Federated Learning Poisoning” 12Arindam Banerjee