Personalized Federated Learning via Sequential Layer Expansion in Representation Learning

Abstract: Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. However, in real-world scenarios, the heterogeneity of data among clients necessitates appropriate personalization methods. In this paper, we aim to address this heterogeneity using a form of parameter decoupling known as representation learning. Representation learning divides deep learning models into 'base' and 'head' components. The base component, capturing common features across all clients, is shared with the server, while the head component, capturing unique features specific to individual clients, remains local. We propose a new representation learning-based approach that suggests decoupling the entire deep learning model into more densely divided parts with the application of suitable scheduling methods, which can benefit not only data heterogeneity but also class heterogeneity. In this paper, we compare and analyze two layer scheduling approaches, namely forward (\textit{Vanilla}) and backward (\textit{Anti}), in the context of data and class heterogeneity among clients. Our experimental results show that the proposed algorithm, when compared to existing personalized federated learning algorithms, achieves increased accuracy, especially under challenging conditions, while reducing computation costs.

• The paper introduces a novel Sequential Layer Expansion method to address data and class heterogeneity in federated learning.
• It decouples deep learning models into granular layers using Vanilla and Anti-scheduling approaches for adaptive representation learning.
• Experimental results on CIFAR-100 and Tiny-ImageNet show improved accuracy with reduced communication and computational overhead.

Personalized Federated Learning via Sequential Layer Expansion

The study titled "Personalized Federated Learning via Sequential Layer Expansion in Representation Learning" presents an innovative methodology within the domain of Personalized Federated Learning (PFL). The methodology is aimed at addressing the significant challenge of data heterogeneity among clients in federated learning (FL) frameworks. Traditional federated learning architectures struggle with the non-IID nature of client data, often leading to biased and less robust global models. This paper proposes a nuanced methodology that segments deep learning models into more granular components than the typical 'base' and 'head' components used in conventional representation learning.

The crux of the proposed method involves a strategic decoupling of model layers using a novel approach dubbed as Sequential Layer Expansion. The model is effectively divided into densely layered parts, enabling a more targeted and efficient learning and personalization process. This goes beyond the basic bifurcation into 'base'—constituted of commonly shared features—and 'head' layers—dedicated to uniquely identifying client-specific features. The proposed framework aims not only at providing solutions to data heterogeneity but also class heterogeneity prevalent among client devices in the federated settings.

The study explores two layer scheduling frameworks: Vanilla and Anti-scheduling. Vanilla scheduling begins by unfreezing the model's shallowest layers initially and progressively advances towards deeper layers—intended to extract foundational low-level features early. Conversely, Anti-scheduling starts unraveling from the deepest layers to extract complex high-level features early in the training phase. The strategic selection between these scheduling approaches allows the trained models to focus adaptively on the nature of heterogeneity—be it data-centric or class-centric.

Key numerical results from the experiments underscore the efficiency of the proposed methodologies. In challenging scenarios characterized by pronounced data and class heterogeneity, the proposed PFL approach demonstrates superior accuracy when compared to existing methodologies, coupled with a notable reduction in both communication overhead and computational requirements. Specifically, in high heterogeneity contexts on complex datasets like CIFAR-100 and Tiny-ImageNet, Anti-scheduling is shown to excel in balancing the trade-off between computational cost and model accuracy, outstripping previous representation learning-based approaches in personalized federated learning.

The implications of this study extend to both practical and theoretical spectrums within AI and PFL. Practically, the nuanced approach for dividing and scheduling model layers can lead to more efficient model training and generalization in real-world scenarios where data privacy regulations necessitate the use of federated learning. On a theoretical level, the research fosters a deeper exploration into the interaction of model component structures and learning processes, suggesting possible expansions to the concept of curriculum learning within federated settings.

Looking ahead, this work provides a foundation for further exploration into adaptive layer scheduling algorithms that can autonomously decide optimal scheduling based on incoming data. Such extensions could seamlessly integrate into broader AI systems, potentially catalyzing new architectures in federated and personalized learning environments. As federated learning continues to grow, addressing the adaptation of deep networks in diverse and decentralized data settings remains a pivotal area for future research development.