SAFARI: Sparsity-Enabled Federated Learning With Limited and Unreliable Communications

Mao, Yuzhu; Zhao, Zihao; Yang, Meilin; Liang, Le; Liu, Yang; Ding, Wenbo; Lan, Tian; Zhang, Xiao-Ping

doi:10.1109/tmc.2023.3296624

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Recently, FL has confronted a wealth of challenges, including significant communication overheads [19,26,27] and data heterogeneity [13]. A variety of recent research initiatives have sought to tackle these obstacles.…”

Section: Introductionmentioning

confidence: 99%

Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt

Zhao,

Shi,

Liu

et al. 2023

Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing &Amp; The 2023 ACM Int

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Federated Learning (FL) is often impeded by communication overhead issues. Prompt tuning, as a potential solution, has been introduced to only adjust a few trainable parameters rather than the whole model. However, current single-modality prompt tuning approaches fail to comprehensively portray local clients' data. To overcome this limitation, we present Twin Prompt Federated learning (TPFL), a pioneering solution that integrates both visual and textual modalities, ensuring a more holistic representation of local clients' data characteristics. Furthermore, in order to tackle the data heterogeneity issues, we introduce the Augmented TPFL (ATPFL) employing the contrastive learning to TPFL, which not only enhances the global knowledge acquisition of client models but also fosters the development of robust, compact models. The effectiveness of TPFL and ATPFL is substantiated by our extensive evaluations, consistently showing superior performance compared to all baselines. CCS CONCEPTS• Human-centered computing → Empirical studies in ubiquitous and mobile computing; Collaborative and social computing;• Computing methodologies → Distributed algorithms.

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