Minhoe Kim

Byoungjun Park, Pedro Porto Buarque de Gusmão, Dongjin Ji et al.

Federated learning is a promising paradigm that utilizes distributed client resources while preserving data privacy. Most existing FL approaches assume clients possess labeled data, however, in real-world scenarios, client-side labels are often unavailable. Semi-supervised Federated learning, where only the server holds labeled data, addresses this issue. However, it experiences significant performance degradation as the number of labeled data decreases. To tackle this problem, we propose \textit{CATCHFed}, which introduces client-aware adaptive thresholds considering class difficulty, hybrid thresholds to enhance pseudo-label quality, and utilizes unpseudo-labeled data for consistency regularization. Extensive experiments across various datasets and configurations demonstrate that CATCHFed effectively leverages unlabeled client data, achieving superior performance even in extremely limited-label settings.

Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong et al.

Federated Learning (FL) enables privacy-preserving multi-source information fusion (MSIF) but is challenged by client drift in highly heterogeneous data settings. Many existing drift-mitigation strategies rely on reference-based techniques--such as gradient adjustments or proximal loss--that use historical snapshots (e.g., past gradients or previous global models) as reference points. When only a subset of clients participates in each training round, these historical references may not accurately capture the overall data distribution, leading to unstable training. In contrast, our proposed Gradient Centralized Federated Learning (GC-Fed) employs a hyperplane as a historically independent reference point to guide local training and enhance inter-client alignment. GC-Fed comprises two complementary components: Local GC, which centralizes gradients during local training, and Global GC, which centralizes updates during server aggregation. In our hybrid design, Local GC is applied to feature-extraction layers to harmonize client contributions, while Global GC refines classifier layers to stabilize round-wise performance. Theoretical analysis and extensive experiments on benchmark FL tasks demonstrate that GC-Fed effectively mitigates client drift and achieves up to a 20% improvement in accuracy under heterogeneous and partial participation conditions.

Jungwon Seo, Minhoe Kim, Chunming Rong

Federated Learning (FL) commonly relies on a central server to coordinate training across distributed clients. While effective, this paradigm suffers from significant communication overhead, impacting overall training efficiency. To mitigate this, prior work has explored compression techniques such as quantization. However, in heterogeneous FL settings, clients may employ different quantization levels based on their hardware or network constraints, necessitating a mixed-precision aggregation process at the server. This introduces additional challenges, exacerbating client drift and leading to performance degradation. In this work, we propose FedShift, a novel aggregation methodology designed to mitigate performance degradation in FL scenarios with mixed quantization levels. FedShift employs a statistical matching mechanism based on weight shifting to align mixed-precision models, thereby reducing model divergence and addressing quantization-induced bias. Our approach functions as an add-on to existing FL optimization algorithms, enhancing their robustness and improving convergence. Empirical results demonstrate that FedShift effectively mitigates the negative impact of mixed-precision aggregation, yielding superior performance across various FL benchmarks.

Woongsup Lee, Ohyun Jo, Minhoe Kim

Deep learning is a potential paradigm changer for the design of wireless communications systems (WCS), from conventional handcrafted schemes based on sophisticated mathematical models with assumptions to autonomous schemes based on the end-to-end deep learning using a large number of data. In this article, we present a basic concept of the deep learning and its application to WCS by investigating the resource allocation (RA) scheme based on a deep neural network (DNN) where multiple goals with various constraints can be satisfied through the end-to-end deep learning. Especially, the optimality and feasibility of the DNN based RA are verified through simulation. Then, we discuss the technical challenges regarding the application of deep learning in WCS.