Fabian Stricker

Fabian Stricker, David Bermbach, Christian Zirpins

Federated Learning (FL) is a paradigm for training machine learning (ML) models in collaborative settings while preserving participants' privacy by keeping raw data local. A key requirement for the use of FL in production is reliability, as insufficient reliability can compromise the validity, stability, and reproducibility of learning outcomes. FL inherently operates as a distributed system and is therefore susceptible to crash failures, network partitioning, and other fault scenarios. Despite this, the impact of such failures on FL outcomes has not yet been studied systematically. In this paper, we address this gap by investigating the impact of missing participants in FL. To this end, we conduct extensive experiments on image, tabular, and time-series data and analyze how the absence of participants affects model performance, taking into account influencing factors such as data skewness, different availability patterns, and model architectures. Furthermore, we examine scenario-specific aspects, including the utility of the global model for missing participants. Our experiments provide detailed insights into the effects of various influencing factors. In particular, we show that data skewness has a strong impact, often leading to overly optimistic model evaluations and, in some cases, even altering the effects of other influencing factors.

Fabian Stricker, Jose A. Peregrina, David Bermbach et al.

Performance evaluation is essential for assessing the quality of machine learning (ML) models and guiding deployment decisions. In federated learning (FL), assessing the performance is challenging because data are distributed across participants. Consequently, the coordinator must rely on locally computed evaluation metrics and aggregate them to assess the global model. A key challenge is that common aggregation strategies, such as weighted averaging based on the local samples per participant, do not always produce the same results as centralized evaluation. Existing definitions of performance evaluation are largely tailored to accuracy and do not generalize to other metrics, leading to inconsistencies between participant-based and centralized evaluation. However, such discrepancies are inconsistent with the FL objective and lead to a wrong calculation of the metric. To address this issue, we examine the underlying reasons for these discrepancies and propose FLAM, a performance evaluation method based on aggregatable measures that yields the same results as centralized evaluation without the need for a global test dataset.

Fabian Stricker, David Bermbach, Christian Zirpins

Federated learning (FL) is a new paradigm for training machine learning (ML) models without sharing data. While applying FL in cross-silo scenarios, where organizations collaborate, it is necessary that the FL system is reliable; however, participants can fail due to various reasons (e.g., communication issues or misconfigurations). In order to provide a reliable system, it is necessary to analyze the impact of participant failures. While this problem received attention in cross-device FL where mobile devices with limited resources participate, there is comparatively little research in cross-silo FL. Therefore, we conduct an extensive study for analyzing the impact of participant failures on the model quality in the context of inter-organizational cross-silo FL with few participants. In our study, we focus on analyzing generally influential factors such as the impact of the timing and the data as well as the impact on the evaluation, which is important for deciding, if the model should be deployed. We show that under high skews the evaluation is optimistic and hides the real impact. Furthermore, we demonstrate that the timing impacts the quality of the trained model. Our results offer insights for researchers and software architects aiming to build robust FL systems.