Federated AI for the Enterprise: A Web Services Based Implementation

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

“…Figure 1a show its overall process of one training round, and Figure 1b shows its system architecture. These figures emphasize four novel contributions made in FLSys, compared with existing FL systems [3], [39], [35], [13], [37]: (1) FLSys allows the phones to self-select for training when they have enough data and resources; (2) FLSys has an asynchronous design (Figure 1a), in which the server in the cloud tolerates client failures/disconnections and allows clients to join training at any time. (3) FLSys supports multiple DL models that can be used concurrently by multiple apps; each phone trains and uses only the models for which it has subscribed; and (4) FLSys acts as a "central hub" on the phone to manage the training, updating, and access control of FL models used by different apps.…”

“…Finally, the procedure checks if a new round should be started by evaluating the Start New Round policy. If a new round is to be started, a new deadline will be set (lines [33][34][35][36]. Otherwise, the procedure terminates.…”

“…Mobile operating system (OS) providers use FL in their OSs for applications such as next word prediction on the keyboard [39], but their solutions are applicationspecific and lack system details. Verma et al [35] and Liu et al [27] introduce web-services based FL architectures, which are not tailored to mobile devices. An under construction FL system [13] does not focus on application-system co-design aspects such as efficient data collection or scalability.…”

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

“…The charging stations work as clients in FL process, and only exchange trained model with the charging station provider without exchanging raw user data. Verma et al [46] propose a web service-based implementation of FL for cross domain enterprise data sharing. Yu et al [47] introduce a FL-based proactive content caching scheme for edge computing.…”

Federated Learning for Coalition Operations

“…In order to deal with them in a consistent manner, giving the wide diversity of AI models and their applicability domains, we can divide any AI enabled task into the four categories shown in Table I based on the input and output used by the task. A more detailed justification of this categorization is found in [7]. Based on the above categories and the two modes for coalition federated learning, we can identify the following challenges that need to be overcome for both modes of operation: Varying Data Quality: For training data that consists of raw input (categories II and IV in Table I), the input modality could vary from site to site.…”