FedGPA is proposed, a hierarchical framework in which clients with comparable resources and identical architectures form a group and a server mediates knowledge transfer across groups of diverse models, and at its core, the lightweight, model-agnostic Aligned Co-decision (Alco) Unit aligns class-level prototypical information across groups to bridge heterogeneous architectures.
Liyinglan Liu, Zikai Xiao, Zi-Han Chen et al.· Journal of Machine Learning...· 0 citations
LDT-Coord is proposed, a networked coordination framework built upon a lightweight digital twin that achieves a task success rate comparable to conventional coordination methods while reducing communication overhead by more than 70x and maintaining robustness under LLM heterogeneity.
A systematic survey of media communication technologies for 6G vision communication is presented by revisiting the evolution of communication and media technologies and clarifying the intrinsic relationship between media content processing and wireless transmission.
Bingyan Xie, Longyu Zhou, Zi-Han Chen et al.· 0 citations
Federated learning (FL) applications normally employ large deep learning (DL) models, resulting in excessive communication overhead in the deployment of FL over resource-constraint mobile edge networks. To achieve better scalability for DL-based FL, we capitalize on both the asymmetric nature of mobile networks and the distinct effects of partial transmissions on FL training for the global and local models. We propose Fed-DynAmal, an FL framework that decreases the number of parameters transmitted in the uplink (clients-to-server) while concurrently achieving better model performance. The underlying idea is that each selected client sends a partial DL model to the server by omitting several sub-blocks from the trained local model. Crucially, we drop the assumption that transmitted local models can still be used for inference, thereby allowing for greater model variability. At the server, we introduce amalgamation, a process to merge different partial local models into an inference-viable full model. Essentially, amalgamation is a bridge for performing aggregation at the sub-block level. Interestingly, as the key takeaway, communication efficiency versus model performance is not necessarily a trade-off in FL: Our extensive experiments show that Fed-DynAmal can effectively improve communication efficiency while still concurrently achieving higher accuracy and enhanced robustness.
Zihan Chen, H. Yang, Tony Q. S. Quek et al.· IEEE Transactions on Cogniti...· 0 citations
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