An Important Discussion on Federated Learning Methodologies

This paper is available on arxiv under CC 4.0 license. Authors: (1) Berrenur Saylam; (2) Ozlem Durmaz ¨ ˙Incel. Table of Links Highlights Abstract & Introduction Federated Learning: Collaborative and Privacy-Preserving Model Training State of the Art Federated Learning Software Platforms and Testbeds Sensors and Edge-Sensing Devices Federated Learning Applications on Sensing Devices Discussion Conclusions, Acknowledgment, and References 6. Discussion The discussion section introduces several points that have emerged from the literature review on applying FL methodologies to sensing devices. Firstly, it is noteworthy that most studies in the literature predominantly focus on synchronized FL, where training and model transfer occur in a synchronized manner Gao et al. (2021). However, the reality of systems and data heterogeneity often leads to asynchronous training and model transfer, posing challenges in scaling federated optimization. To enable efficient and scalable federated optimization, future research should explore techniques and algorithms that effectively handle the asynchrony inherent in FL settings. Another key point to consider is the assumed labelled data in FL when using supervised algorithms, while on-device data is typically unlabelled Jing et al. (2019). This presents a challenge when applying FL methodologies directly to on-device sensing tasks. Future research should concentrate on developing techniques that can effectively leverage the unlabelled on-device data and explore strategies for incorporating label acquisition processes within the FL framework. It is worth noting that many FL studies in the literature have primarily focused on simulation environments. While simulations provide controlled and reproducible settings, there is a need for more studies that adapt FL methodologies to real-world applications. Real-world deployment of FL on sensing devices introduces additional challenges, including communication constraints, privacy concerns, and device heterogeneity. Further research should aim to bridge the gap between simulation and real-world deployment, ensuring the practicality and effectiveness of FL methodologies in real-world sensing applications. The impact of wireless communication and resource efficiency is a significant aspect when applying FL methodologies to sensing devices. While FL minimizes data transmission by uploading local updates instead of raw data, the size of the trained model parameters, especially with Deep Neural Networks (DNNs), can still be substantial. This poses a challenge for resource-constrained, battery-operated devices that may require numerous wireless communication rounds and iterations. Efficient wireless communication is crucial in distributed machine learning to address the limitations of resource-constrained devices. Various methods can be employed to achieve communication efficiency, such as reducing the size of model updates, optimizing communication frequency, and employing selective client participation. These approaches aim to minimize the data exchanged during model updates, reducing communication overhead. Training DNN models on resource-constrained clients, such as wearable and IoT devices, presents a significant challenge. These devices often have limited computation power, memory, and energy resources. The computational cost of running the full DNN model can be prohibitively high for such devices. Therefore, it is essential to design computationally efficient algorithms, considering the limited processing capabilities of these devices. Optimizing processing speed becomes crucial as it directly impacts the algorithm’s throughput, latency, and response time. Furthermore, personalization is a key aspect that holds potential in applying FL methodologies to sensing devices. The ability to tailor the learning process and model parameters according to individual device characteristics and user preferences can significantly enhance the performance and user experience of FL-based applications. One approach to personalization in FL is to adapt the training process to individual sensing devices’ specific capabilities and constraints. As mentioned, different devices possess varying computational power, memory capacity, and energy resources. By considering these device-specific factors during the training phase, it is possible to optimize the learning process and model updates to suit the capabilities of each device. This personalized training approach can improve performance and energy efficiency by leveraging individual devices’ strengths and limitations. Another aspect of personalization in FL is user-centric customization. Sensing devices are often intimately connected to their users, capturing personal data and insights. By incorporating user preferences and characteristics into the learning process, FL can create personalized models that cater to specific user needs. This can be particularly relevant in applications such as health monitoring or affective computing, where individual variations and preferences play a significant role. Personalizing the FL models based on user-specific data can greatly enhance the accuracy and relevance of the generated insights. Privacy is a critical concern in FL, especially when dealing with personal data. However, personalization and privacy can coexist through the adoption of privacy-preserving techniques. Techniques such as differential privacy, federated encryption, and secure aggregation can be employed to protect sensitive user data while still enabling personalized learning. By ensuring that personalization is conducted privacy-consciously, FL can strike a balance between customization and data protection, gaining user trust and fostering broader adoption. Moreover, personalization in FL can extend beyond individual devices and users to cater to specific application domains. Different sensing applications, such as medical, environmental, automotive, industrial, aerospace, and agricultural, may have unique requirements and objectives. By tailoring the FL methodologies and model architectures to the specific demands of each domain, personalized FL solutions can be developed to address the distinct challenges and opportunities presented by different application contexts. This paper is under a CC 4.0 license. available on Arxiv