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Federated learning (FL) on memory-constrained edge devices faces a dilemma: first-order (FO) optimization (i.e., backpropagation) demands substantial memory, whereas zeroth-order (ZO) optimization suffers from severe convergence slowdown. To resolve this dilemma, we introduce HO-FL, a hybrid-order FL framework that tra...
CFLoRA is presented, a federated LoRA scheme that partitions latent LoRA channels into two complementary sets in every communication round, and eliminates bilinear terms in matrix multiplications, making federated aggregation exact.
A polynomial-time algorithm is developed that transforms the original problem into two sub-problems and obtains a sub-optimal solution with a constant approximation guarantee and demonstrates that BALANCE consistently outperforms conventional AD and SD and significantly improves task throughput.
A polynomial-time algorithm is developed that transforms the original problem into two sub-problems and obtains a sub-optimal solution with a constant approximation guarantee and demonstrates that BALANCE consistently outperforms conventional AD and SD and significantly improves task throughput.
In autonomous driving, perception models often struggle to generalize to new environments due to domain shifts. While unsupervised model adaptation offers a feasible solution without labor-intensive manual labeling, existing methods that rely solely on the ego-vehicle's data often lead to inferior pseudo-labeling perfo...
Fluid antenna systems (FAS) have emerged as a promising paradigm for wireless communications, enabling channel reconfigurability that offers a novel spatial degree of freedom. Nevertheless, efficiently acquiring accurate and high-resolution channel state information (CSI) in FAS remains challenging, primarily due to it...
L-shaped SFT is presented, a split fine-tuning framework that removes the need for continuous client participation and introduces one-shot SFT, in which clients upload activations once and then go offline while the server continues optimization over cached representations.
FlexP-SFT is proposed, a novel aggregation-free framework for personalized split federated fine-tuning, which fundamentally eliminates the client-side aggregation process and introduces a layer-flexible alignment strategy to balance personalization and generalization capabilities.
SplitLite is proposed, a communication-efficient split federated LoRA fine-tuning method that exploits the low effective rank structure of consecutive-epoch activation and gradient residuals, thereby significantly reducing both activation uplink and gradient downlink traffic.
This work establishes an efficient optimization framework for SFL under resource-constrained networks that jointly optimizes model splitting and resource allocation to minimize training cost, which is defined as the weighted sum of latency and energy costs.
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