Solving Privacy Challenges in IoT with Federated Learning
We are living in an interconnected world streamlined by connectivity, and IoT is one of the most prominent technological advancements revolutionising how we interact with the world around us, and empowering us to collect and analyse data like never before.
Research shows that by 2027, the number of active IoT systems is expected to reach 29.7 billion. The rapid expansion of this interconnected landscape necessitates a proactive approach to address the privacy challenges inherent in IoT applications.
IoT devices are all around us – in our smart home appliances, manufacturing systems, and transportation. Although diverse in their applications, all IoT systems share a common need for efficient solutions to enable optimal functionality and performance.
In this blog, we’ll first understand the potential of IoT and then explore how federated machine learning can enhance privacy, security and efficiency in IoT systems.
Understanding the Era of IoT
Let’s delve into the intricate evolution of IoT. The Internet of Things (IoT) comprises a sophisticated network of interconnected devices designed to collect, process, and exchange data. The concept of IoT originated in the late 1990s when British technologist Kevin Ashton coined the term to describe a system linking everyday objects to the internet through radio frequency identification (RFID) tags.
An early instance of an IoT device was a Coca-Cola vending machine at Carnegie Mellon University in the early 1980s, capable of reporting its inventory and temperature over the Internet. Since then, IoT has rapidly advanced, driven by the evolution of communication technologies, such as wireless networks, cloud computing, and artificial intelligence.
Today, IoT encompasses diverse devices, including smart home appliances, wearable gadgets, industrial sensors, and autonomous vehicles. These devices find applications across various domains such as healthcare, transportation, agriculture, and smart cities, offering benefits, such as enhanced efficiency, convenience, and innovation.
Emerging Industrial IoT (IIoT) Practices
Industrial IoT (IIoT) is a subset of IoT focusing on IoT technologies’ application in industrial settings, like manufacturing, energy, and logistics. IIoT involves connecting machines, devices, sensors, and software to facilitate data collection, analysis, and automation, ultimately enhancing productivity, quality, safety, and sustainability in industrial operations.
These industrial systems leverage technologies like cyber-physical systems, cloud computing, edge computing, IoT analytics, and artificial intelligence. Major industry verticals employing IIoT include electricity, gas, water, retail, and transportation.
Examples of IIoT systems include:
- Smart Manufacturing: This involves monitoring and controlling machines, equipment, and materials, implementing predictive maintenance, quality assurance, and energy management. Smart manufacturing enhances efficiency, flexibility, and profitability in the manufacturing sector.
- Smart Grid: By collecting and analysing data from power generation, transmission, distribution, and consumption, smart grid implementation incorporates demand response, load balancing, and renewable integration. It also reduces greenhouse gas emissions, power outages, and operational costs in the energy sector.
- Smart Transportation: Connecting vehicles, infrastructure, and users, smart transportation incorporates intelligent traffic management, autonomous driving, and vehicle-to-everything communication. This approach improves mobility, safety, and efficiency in the transportation sector.
It is clear that Industrial IoT systems have large impacts on the environment, society, and governments. In fact, Accenture research shows that IIoT is expected to generate $14 trillion of global GDP. And with such a large impact comes considerable challenges.
Current Privacy Challenges in IoT Environments
IoT data, generated by devices individually or collectively, offers valuable insights for performance improvement, enhanced functionality, or creating new value.
As IoT expands, the accompanying surge in data generation rises significantly. This wealth of information poses privacy risks, as IoT data can include sensitive data. Within an industrial IoT setting, it may expose supply chain health, financial performance, or operational status, opening avenues for sabotage, acquisition, or a competitive advantage.
IoT data can also disclose device configuration, operation, or vulnerability, providing opportunities for hacking, tampering, and sabotage. Let’s delve deeper into the key privacy and security challenges inherent in IoT without collaborative machine learning.
Data Breaches and Unauthorised Access
IoT systems involve a complete data lifecycle, encompassing processes like collection, transmission, storage, processing, and sharing. In IoT environments utilising traditional machine learning techniques, information must be gathered from numerous edge devices and consolidated into a central storage and computing location.
These data movements pose privacy risks, leading to concerns about data breaches and unauthorised access. Consequently, such breaches can result in severe consequences, including the exposure of private information, compromising user, device or system security, and damage to organisational and brand reputation.
Data Ownership and Consent
Ensuring user privacy and trust, as well as compliance with legal and ethical regulations, hinges on the clarity and consistency of data ownership and consent in Industrial IoT. Yet, in this complex and dynamic landscape, these aspects are often unclear, ambiguous, or inconsistent due to the diversity of data sources, types, flows, and stakeholders.
In IoT systems lacking transparency and accountability, particularly those without distributed federated learning, the rights and permissions of both data owners and users can be violated. IoT systems need to be backed up with technologies that allow IoT devices to control and benefit from their own data without sharing it with others.
Data Protection and Compliance
Data protection and compliance play a crucial role in averting and alleviating privacy risks and harms within Industrial IoT, while also upholding user rights and preferences. Nevertheless, achieving effective data protection and compliance in Industrial IoT proves to be a persistent challenge due to the heterogeneity, scalability, and mobility inherent in data systems, formats, and environments.
An effective approach to overcoming these challenges involves employing IoT systems that minimise data transmission and storage, eliminating the need for data standardisation or synchronisation.
Federated Learning for IoT and How It Solves Security Challenges
Federated learning (FL) is a collaborative learning method that facilitates decentralised edge devices or nodes to collectively train models without the necessity of transferring or storing raw data in a central server.
Under this paradigm, each device independently trains a local model on its respective data, transmitting only the model updates to a central server. The central server aggregates these updates to construct a global model. This approach ensures the maintenance of data security in federated learning, as the raw data remains stored on the local devices.
Federated learning for IoT constitutes a subset of federated learning, concentrating on the application of federated learning principles to IoT devices, such as sensors, actuators, or smart gadgets, which possess the capability to collect, process, and exchange data.
This on-device machine learning model ensuring IoT data privacy operates as follows:
- A central server initiates a global model and distributes it to a set of participating IoT devices engaged in the federated learning for IoT process.
- Each IoT device autonomously trains the global model using its local data, computing a local model update that captures the disparity between the local and global models.
- The IoT device transmits its local model update to the central server, which aggregates updates from all devices and adjusts the global model accordingly.
- The central server dispatches the updated global model back to the IoT devices, and this cycle continues until the global model converges or reaches the desired accuracy.
Federated learning for IoT contributes significantly to enhancing privacy, security, and overall system efficiency. By preserving data locality, protecting user data, and upholding ownership, it addresses key concerns:
- Data Security: Mitigates the risk of data breaches and unauthorised access by ensuring data remains on IoT devices, avoiding transmission to central servers or other devices. This approach minimises data exposure, reducing the potential for interception, modification, or theft.
- Ownership and Consent: Federated learning empowers IoT devices to maintain control over their data, refraining from sharing it with others. This approach allows data owners and users to decide when and how their data is utilised, with the ability to grant or revoke permissions.
- Data Protection and Compliance: Facilitating robust data protection and compliance, federated learning for IoT minimises data transmission and storage requirements. It eliminates the need for data standardisation or synchronisation, streamlining the complexity and cost associated with data security and regulation. This adaptive approach accommodates the diverse and dynamic nature of IoT data.
Benefits of Federated Learning for Internet of Things
Federated learning not only mitigates the privacy risks associated with traditional machine learning techniques but also unlocks the full potential of IoT, laying the groundwork for a secure and efficient future.
Here is a closer look at the advantages of on-device intelligence through federated learning:
- Enhanced Functionality: Federated learning elevates the functionality of IoT systems by enabling the creation of customised machine learning models. These models can adapt to the specific needs and preferences of IoT users and devices, fostering increased satisfaction, performance and usage.
- Added Value: The value of IoT systems is heightened with federated learning, facilitating the development of innovative and intelligent devices. These enhanced devices generate fresh insights and solutions from IoT data, creating new opportunities and markets for businesses and stakeholders.
- Performance Optimisation: Federated learning enhances the performance of IoT-enabled systems by enabling more accurate and efficient machine learning models to be delivered. By leveraging the rich and diverse data from IoT devices, this approach improves the quality and reliability of services and applications.
- Flexible Scalability: Federated Learning utilises the computational resources of multiple IoT devices across various locations in parallel, enhancing scalability without overburdening a centralised server. The elimination of raw data transmission further reduces communication costs, especially in low-bandwidth IoT networks.
Let’s Join Forces to Unlock the Value of IoT Systems
Safely employing the full potential of IoT systems demands efficient solutions that facilitate optimal functionality and performance for businesses. OctaiPipe is revolutionising IoT through its cutting-edge federated ML technology.
Going beyond the mere facilitation of streamlined deployment and management of FL models on IoT devices, OctaiPipe integrates Edge MLOps, Edge ML Solutions, and Edge Federated Learning by training AI on edge devices. This comprehensive solution ensures the efficient lifecycle management of machine learning models, operating directly on devices and thereby reducing data transfer, computing costs, and privacy risks.
OctaiPipe’s commitment to privacy is evident in its strategic minimisation of data transfer during model deployment, providing a secure and privacy-centric solution at the forefront of Edge ML innovation.
Are you ready to elevate your IoT data science capabilities? Reach out to us to experience the power of OctaiPipe.