Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. However, new approaches to FL often discuss their contributions involving small deep-learning models only and focus on training full models on clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. Typically, FMs have already been pre-trained across a wide variety of tasks and can be fine-tuned to specific downstream tasks over significantly smaller datasets than required for full model training. However, access to such datasets is often challenging. By its design, FL can help to open data silos. With this survey, we introduce a novel taxonomy focused on computational and communication efficiency, the vital elements to make use of FMs in FL systems. We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications, elaborate on the readiness of FL frameworks to work with FMs and provide future research opportunities on how to evaluate generative models in FL as well as the interplay of privacy and PEFT.

Large Language Models (LLM) and foundation models are popular as they offer new opportunities for individuals and businesses to improve natural language processing, interact with data, and retrieve information faster. However, training or fine-tuning LLMs requires a vast amount of data, which can be challenging to access due to legal or technical restrictions and may require private computing resources. Federated Learning (FL) is a solution designed to overcome these challenges and expand data access for deep learning applications. This paper takes a hardware-centric approach to explore how LLMs can be brought to modern edge computing systems. Our study fine-tunes the FLAN-T5 model family, ranging from 80M to 3B parameters, using FL for a text summarization task. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions. Our contribution is twofold: First, we evaluate the current capabilities of edge computing systems and their potential for LLM FL workloads. Second, by comparing these systems with a data-center GPU, we demonstrate the potential for improvement and the next steps toward achieving greater computational efficiency at the edge.

Federated Machine Learning (FL) has received considerable attention in recent years. FL benchmarks are predominantly explored in either simulated systems or data center environments, neglecting the setups of real-world systems, which are often closely linked to edge computing. We close this research gap by introducing FLEdge, a benchmark targeting FL workloads in edge computing systems. We systematically study hardware heterogeneity, energy efficiency during training, and the effect of various differential privacy levels on training in FL systems. To make this benchmark applicable to real-world scenarios, we evaluate the impact of client dropouts on state-of-the-art FL strategies with failure rates as high as 50%. FLEdge provides new insights, such as that training state-of-the-art FL workloads on older GPU-accelerated embedded devices is up to 3x more energy efficient than on modern server-grade GPUs.