This paper explores how structured topology can approximate full client-side coordination through decentralized communication among neighboring nodes and further examines how topology, workload, and non-IID data affect communication and learning efficiency in edge environments.
Split Federated Learning (SFL) has emerged as a pivotal paradigm for privacy-preserving distributed training on resource-constrained edge devices by partitioning neural networks between clients and a server. A critical design choice in SFL is the split layer, which determines the computation distribution and the semant...
Ai-Jing Li, Ya-Wen Li, Guan-Hua Ye et al.· Proceedings of the Thirty-Fi...· 0 citations
In gossip learning, a network of nodes trains a shared model collaboratively, without a central coordinator, by repeatedly exchanging parts of their local models. The state-of-the-art protocol, Partitioned Token Gossip Learning (PTGL) of Heged{\"u}s et al., splits the weight matrix into S fixed partitions and dissemina...
Fabien Mathieu, A.-Q. Pham, Maria Gradinariu Potop-Butucaru et al.· 0 citations
ReTri, a bidirectional All-to-All schedule for ORNs based on the Trivance algorithm is presented, a bidirectional All-to-All schedule for ORNs based on the Trivance algorithm that improves completion time and improves reconfigurable Bruck by up to 2.1×.
Anton Juerss, Stefan Schmid· Conference on Applications,...· 0 citations
HybridFLow is presented, a closed-loop SDN-driven orchestration framework that integrates network-layer intelligence directly into hybrid FL and generates calibrated per-client communication-time estimates before each training round and uses them to partition clients into synchronous and asynchronous groups while balan...
Osama Abu Hamdan, Rabina Pandey, Hao Che et al.· 0 citations
A personalized DSC framework that cuts off cross-task interference by limiting the system to absorbing complementary knowledge while actively blocking mismatched parameter updates, and which achieves a 4.77% global relative improvement over the no-aggregation baseline and outperforms decentralized FedAvg, FedAMP, and h...
Linqi Yin, Tie-Jun Lv, Wei-Cai Li et al.· IEEE Transactions on Communi...· 0 citations
Distributed training across a wide area network (WAN) is challenging, as continuous parameter exchange by islands of compute is constrained by limited bandwidth, high latency, and uneven topology. We propose making the network an active participant in training. On the systems side, such networks should leverage (i) mul...
Nihar B. Shah, Ben Blier· 0 citations
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