{"id":"b0c57197-e169-4ccb-9edb-b5515901d2bf","arxiv_id":"2504.14667","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"SflLLM fine-tunes LLMs over wireless networks by splitting the model, using LoRA adapters, and jointly optimizing communication resources, split point, and rank to cut training latency.","lead":"This paper combines split federated learning with LoRA to fine-tune large language models on edge devices, splitting the model between clients and a main server and aggregating client adapters on a federated server. It then jointly optimizes subchannel assignment, transmit power, split point, and LoRA rank to reduce total training time.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"P4's rank selection rests on an unvalidated offline E(r) curve; if E(r) does not transfer to the deployment data, the reported latency reduction from adaptive rank selection is miscalibrated.","rationale":"The reader's conditional verdict is well-aimed. The strongest claim -- reduced training latency via joint resource allocation and adaptive rank selection -- flows through the delay objective, and the only quantity in that objective not derived from the system model or measured in deployment is E(r). The paper treats E(r) as depending only on r and estimates it from representative pretraining; no evidence is given that the curve carries over to the deployment data distribution, target accuracy, or client heterogeneity. Because P4 enumerates candidate ranks, an incorrect E(r) can reorder the ranks and change the reported latency comparison; this is a correctness risk, not merely a missing baseline. The BCD decomposition and the convexity argument for P2 are otherwise plausible, and the accuracy comparison to centralized LoRA is reported. Secondary issues -- Algorithm 1's loop variable E appears to count local steps while Eq. (17) uses E as global rounds, and labels are uploaded to the main server despite privacy claims -- do not displace the E(r) concern. Since this is the same weakest assumption the reader identified and the appropriate remedy is to validate E(r), the verdict should remain conditional until that check is performed.","tokens_in":22129,"tokens_out":9961,"duration_ms":96207,"concrete_test":"Re-run the Section VII latency evaluation with E(r) measured instead of assumed: for each candidate rank r, fix the proposed resource allocation and split, train SflLLM on the actual (or a held-out non-IID) client partition until the target validation PPL is reached, record the observed number of global rounds E_obs(r), and recompute Eq. (17) and the P4 rank choice using E_obs(r). If the optimal rank changes, or if the latency gap between the proposed scheme and baseline d shrinks by more than 20% relative to the reported figures, the offline-E(r) assumption is load-bearing and the rank-selection claim is not transferable.","verdict_should_be":"UNCHANGED","load_bearing_attack":"In Eq. (17) the total delay is T = E(r)(I T_local + max_k T_f^k), and subproblem P4 (Eq. 26) picks the LoRA rank by minimizing this expression over r. Section VI.C says E(r) -- the number of global rounds needed to reach the target accuracy -- 'can be estimated offline through pretraining on a representative dataset,' but the paper gives no fitted form, no E(r) table, and no transferability analysis; the theoretical rank-convergence analysis promised in Section I is explicitly deferred to future work in Section VIII. The rank-selection contribution therefore depends entirely on E(r) being the correct convergence-round count for the actual deployment: the deployment's data distribution, non-IID partition, target accuracy, and optimizer settings. If E(r) is measured on E2E/GPT-2 but the framework is deployed elsewhere, P4 can select a different rank than the true optimum, and every latency comparison that isolates rank selection (especially baseline d in Section VII.C) is miscalibrated. This is a correctness risk in the central claim, not just a missing baseline: the reported latency reductions could be an artifact of the assumed E(r) curve rather than of the proposed optimization.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes SflLLM, a split federated learning framework that combines model splitting with LoRA for fine-tuning large language models on resource-constrained edge networks. A joint optimization problem is formulated to minimize total training delay by jointly selecting subchannel allocation, transmit power, model split point, and LoRA rank. The authors propose a block coordinate descent algorithm with a greedy subchannel allocation, a convex reformulation of the power control subproblem, and exhaustive search for split point and rank. Simulations on the E2E dataset with GPT-2 variants show comparable accuracy to centralized LoRA and reduced training latency relative to several baselines.","tokens_in":22310,"tokens_out":5107,"duration_ms":45278,"significance":"If the delay model and optimization are validated, the framework addresses a practically important problem: distributed fine-tuning of LLMs over heterogeneous wireless edge networks. The paper contains several sound and useful components: the convex reformulation of the power control subproblem (Eqs. (22)-(24)) appears internally consistent, the complexity analysis in Section VI.D is explicit, and the accuracy comparison to centralized LoRA in Table IV (PPL deviation within 0.001) is a clean sanity check. The main significance of the claimed result, however, rests on the adaptive LoRA rank selection, and that claim is currently built on an empirically fitted E(r) curve whose transferability is not established.","major_comments":[{"comment":"The total delay T in Eq. (17) multiplies per-round latency by E(r), the number of global rounds required to reach target accuracy, and subproblem P4 in Eq. (26) selects the LoRA rank by minimizing this expression over r. Section VI.C states only that E(r) 'can be estimated offline through pretraining on a representative dataset,' but the paper gives no fitted form, no table of E(r) values, and no transferability analysis across data distributions, non-IID partitions, target accuracy levels, or deployment channel conditions. Because E(r) is the only rank-dependent convergence term in the objective, the selected rank and the reported latency reductions (especially the comparison with baseline d in Section VII.C) are calibrated to this assumed curve. If E(r) does not transfer to the actual deployment, P4 can select a non-optimal rank and the stated latency improvement is an artifact of the assumed function rather than a property of the proposed optimization. This is a load-bearing gap for the paper's central claim and requires either a theoretical characterization of E(r), a robust formulation, or a validation study across deployment conditions.","section":"Section VI.C, Eq. (17), P4 (Eq. (26))"},{"comment":"The second contribution bullet in Section I claims that the paper 'theoretically analyze[s] how the rank of the LoRA module affects training overhead and convergence speed.' However, Section VI.C does not provide any theoretical convergence analysis; it instead relies on an offline empirical estimate of E(r). Section VIII explicitly defers 'analyzing the effect of rank selection on convergence through theoretical derivations' to future work. This is an internal inconsistency between the claimed contribution and the delivered content. The authors should either supply the theoretical analysis or revise the contribution statement and the optimization formulation to present E(r) as an empirical input whose estimation and validation are part of the method.","section":"Section I (contribution bullet), Section VI.C, Section VIII"},{"comment":"The evaluation protocol for E(r) in the baselines is unspecified. Baseline d is described as 'random rank selection' with the proposed subchannel, power, and split allocation. For the latency computation of this baseline, it is not stated whether the randomly chosen rank is assigned the same fitted E(r) curve, an independent convergence measurement, or some other value. If the fitted E(r) is used for randomly chosen ranks as well, then the comparison between the proposed rank selection and baseline d circularly favors the fitted rank; if a different protocol is used, it should be described explicitly. The same ambiguity applies to the other baselines when they involve rank selection. A clear and consistent protocol for assigning E(r) to all compared schemes is needed before the latency comparisons can be interpreted.","section":"Section VII.C, baseline d; Section VI.C"},{"comment":"The paper asserts that the proposed BCD algorithm 'reliably converges to a stable and effective solution within a finite number of iterations, regardless of initialization,' while also stating that formal convergence guarantees are precluded. No convergence plots of the BCD objective across iterations, no initialization sensitivity study, and no comparison of different initializations are presented in Section VII. As the algorithm is a heuristic, the empirical convergence claim is currently unsupported by the exhibited results. The authors should add a convergence study (e.g., objective value versus iteration count for several random initializations) or temper the claim to match the evidence.","section":"Section VI.D, Algorithm 3"}],"minor_comments":[{"comment":"The captions of Figs. 6, 7, and 8 are identical to the caption of Fig. 4 ('The effect of LoRA rank on the number of steps required to reach the target loss value') and do not describe the actual plotted quantities (latency versus clients' compute power, main server compute capability, and transmit power, respectively). These captions should be corrected.","section":"Section VII.C, Figs. 6-8"},{"comment":"Table III contains a long sequence of corrupted glyph strings ('/uni00000013/uni00000015/...') in the row labels and some numeric entries, making part of the table unreadable. The table should be regenerated with a proper font encoding.","section":"Table III"},{"comment":"Two bullets in the decision-variable list are both labeled 'r': the first defines the binary subchannel allocation variables r_i,s_k and r_i,f_k, and the second defines the integer LoRA rank r. Using the same symbol for both is confusing; the subchannel variables should be renamed (e.g., a_i,s_k and a_i,f_k) or the rank should be denoted differently.","section":"Section V.A, bullet list"},{"comment":"There are several typographical errors in the introduction, including 'has has significantly advanced,' 'lnternet of things,' and 'A appropriately designed.' These should be corrected in a copyediting pass.","section":"Section I, first paragraph"}],"recommendation":"major_revision","confidential_remarks":"The main obstacle to acceptance is the unvalidated E(r) curve and its role in the rank-selection and latency claims. If the authors can add either a theoretical convergence analysis, a robust formulation not requiring exact E(r), or a thorough transferability study across datasets and deployment conditions, the paper could be acceptable. The corrupted Table III should also be fixed before any resubmission; it may indicate a production/pipeline problem that the editorial office should check."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Zhao et al. propose SflLLM, a split federated learning framework for fine-tuning LLMs with LoRA, and formulate a joint optimization of subchannel allocation, power control, split point, and LoRA rank to minimize total training delay. The jointly optimized rank selection is the genuinely new piece; the rest of the architecture follows SplitLoRA and prior SFL resource allocation work, and the paper is honest about that lineage.\n\nWhat the paper does well: the delay model is explicit and mostly sensible; the power control subproblem is reformulated as convex with auxiliary variables, and that derivation looks internally consistent; the accuracy comparison to centralized LoRA is clean, with perplexity deviations within 0.001. The systems framing — client compute, wireless uploads, server-side training, aggregation — is clear enough to reproduce the latency model.\n\nThe soft spot is the one the reader flagged, and it is real. The total delay in Eq. (17) multiplies per-round latency by E(r), the number of global rounds to target accuracy. Section VI.C says E(r) 'can be estimated offline through pretraining on a representative dataset,' but no fitted form, no table, no transferability analysis is given, and the theoretical rank-convergence analysis promised in the introduction is explicitly deferred to the conclusion. Since P4 selects the rank by minimizing over this E(r), the reported latency reductions from rank optimization (baseline d) are only as good as E(r)'s transferability to the deployment data distribution, non-IID partition, and target accuracy. That is a correctness risk in a central claim, not just a missing baseline. It is fixable: tabulate E(r), test transfer across datasets and non-IID settings, or derive a bound.\n\nOther, smaller issues: no comparison against SplitLoRA itself or against prior SFL resource allocation schemes (e.g., AdaptSFL), and no code or data release. The convergence plots in Figs. 3–4 are on E2E/GPT-2 only, so the generality of the rank effect is not demonstrated.\n\nWho this is for: readers working on edge LLM fine-tuning, wireless federated learning, or split learning resource management. They will get a useful framework and a clear optimization template, but should treat the rank-selection latency gains as provisional until E(r) is characterized.\n\nRecommendation: this deserves peer review. It is a serious systems contribution with a sound optimization core. I would ask for revision rather than desk reject: make E(r) explicit and validate its transferability, add the missing baselines, and ideally release code.","headline":"Solid systems paper with a genuinely new joint optimization, but the rank-selection result leans on an unvalidated offline convergence curve that needs to be made explicit or the reported latency gains from rank selection cannot be trusted.","tokens_in":22901,"tokens_out":2367,"would_cite":false,"duration_ms":20418,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Split federated learning with LoRA matches centralized fine-tuning accuracy on GPT-2 while cutting training latency by up to 60%.","keywords":["split federated learning","LoRA","large language models","edge networks","resource allocation","training latency minimization","model splitting","communication-computation tradeoff"],"falsifier":"Run SflLLM on a different downstream task, or with a non-IID client data split, after fitting $E(r)$ on E2E; then record the actual number of global rounds to the target loss for each candidate rank. If the observed $E(r)$ curve differs enough to change which rank minimizes total training delay, the reported latency reductions do not generalize.","tokens_in":21893,"feed_emoji":"📶","tokens_out":11297,"duration_ms":87762,"temperature":0.7,"pith_summary":"This paper establishes that split federated learning can be made practical for fine-tuning large language models on resource-constrained edge devices when combined with low-rank adaptation (LoRA). It builds a total-training-delay model that couples communication and computation through two knobs: where the model is split between clients and a main server, and what LoRA rank is used. The paper then solves the resulting mixed-integer optimization by alternating a greedy subchannel assignment, a convex power-allocation step, and exhaustive searches over split layer and rank. Simulations on GPT-2 with the E2E dataset show the framework reaches final test perplexity within 0.001 of centralized LoRA fine-tuning while sharply reducing client-side computation, and the optimized resource allocation cuts total training latency by up to 60% relative to a random-allocation baseline.","feed_headline":"Split federated learning trims LLM training delay by 60%","feed_subtitle":"Combining model splitting with LoRA matches centralized fine-tuning accuracy while cutting client compute and latency.","key_machinery":"The load-bearing object is the delay identity in Eq. (17): total training delay is the product of the number of global rounds $E(r)$ and the sum of per-round time $I T_{\\text{local}}$ plus the slowest adapter upload, with $T_{\\text{local}}$ itself the sum of bottleneck client forward/upload time, server forward/backward time, and bottleneck client backward time. Every term is an explicit function of the split vector $\\mu$ (which layers live on the client side), the LoRA rank $r$ (which controls trainable parameters, upload size, and convergence speed), subchannel assignment, and transmit power. This single expression turns 'make fine-tuning fast' into a mixed-integer nonconvex program, and the paper's block-coordinate-descent algorithm attacks it by fixing three variable blocks at a time: greedy allocation for subchannels, a log-convexified convex solve for power, and exhaustive search over the small integer sets of split layers and candidate ranks.","core_discovery":"The central claim is that SflLLM, a split federated learning framework for LLMs, achieves accuracy essentially equal to centralized fine-tuning while making client-side training feasible on low-resource devices. The vehicle is a delay objective, $T = E(r)(I T_{\\text{local}} + \\max_k T_k^f)$, where $E(r)$ is the number of global rounds needed to reach a target loss, $I$ the local steps per round, $T_{\\text{local}}$ the slowest client's forward-plus-upload time plus server forward and backward time plus slowest client's backward time, and $T_k^f$ the time to upload client LoRA adapters to a federated server. Minimizing this objective chooses subchannel assignment, transmit power, the layer at which the model is split, and the LoRA rank. The reported result is that this joint choice reduces training latency by up to 60% versus random allocation and, in the tested regime, rank selection contributes more to the latency saving than communication tuning does.","pith_inferences":["The paper leaves rank-dependent convergence as an empirically fitted curve; a closed-form bound on $E(r)$ as a function of rank and data heterogeneity would let the same delay model be re-optimized online during training instead of only offline.","Because the delay model neglects downlink broadcasts and activation-gradient downloads, its predicted optimal rank and split point could shift in regimes where those costs are large; adding downlink terms is a direct testable extension.","Allowing each client its own split layer, rather than one global split point, would likely further reduce straggler losses in heterogeneous networks, at the price of a larger search space the paper does not explore.","The reported results are at GPT-2 scale on a single dataset; whether the accuracy and latency tradeoffs persist for larger open-weight LLMs or other tasks is not established and could be tested with the same framework."],"forward_implications":["A client that only holds the front part of a transformer can fine-tune an LLM to roughly the same final perplexity as centralized LoRA training, with the tested deviation below 0.001.","Increasing LoRA rank accelerates convergence but raises per-step compute and upload traffic, so the delay-minimizing rank is an interior value that the exhaustive search finds rather than a fixed hyperparameter.","The joint allocation shifts its emphasis with the bottleneck: when bandwidth is scarce, subchannel and power optimization dominate the gain; when client compute is weak, split-point selection dominates.","The complete optimization loop has complexity $O(\\tau_{\\max} K M^2)$, making it implementable in a network controller with modest iteration counts."],"supporting_citations":[{"why":"Introduces split federated learning, the paradigm SflLLM extends to LLMs.","marker":"[13]"},{"why":"Defines low-rank adaptation (LoRA), the trainable-parameter mechanism whose rank is a decision variable.","marker":"[14]"},{"why":"Prior combination of LoRA with split learning that this work extends specifically to SFL for LLMs.","marker":"[16]"},{"why":"Establishes split learning as the client/server computation-splitting baseline.","marker":"[10]"},{"why":"Joint split-point and bandwidth allocation baseline that the resource allocation scheme generalizes to LLM fine-tuning with LoRA.","marker":"[35]"},{"why":"Parallel split learning with heterogeneous-device resource management, used as comparison for the optimization design.","marker":"[36]"},{"why":"E2E restaurant-domain dataset used to measure convergence and final perplexity.","marker":"[39]"},{"why":"GPT-2 architecture (GPT2-S and GPT2-M) used in the simulations.","marker":"[40]"}],"fun_headline_variants":["Split federated learning + LoRA: 60% faster LLM edge training","Joint split-rank optimization cuts LLM training delay by 60%","LoRA-enabled split federated learning matches accuracy, cuts latency","SflLLM: Edge-friendly split federated learning slashes LLM training time"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The delay formula assumes that the fitted curve $E(r)$, giving the number of training rounds needed to reach the target accuracy for each LoRA rank, transfers from an offline representative dataset to the actual deployment data, target accuracy, and channel conditions without re-measurement.","fun_headline_variants_meta":{"raw":{"variants":["Split federated learning + LoRA: 60% faster LLM edge training","Joint split-rank optimization cuts LLM training delay by 60%","LoRA-enabled split federated learning matches accuracy, cuts latency","SflLLM: Edge-friendly split federated learning slashes LLM training time"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000516,"raw_usage":{"total_tokens":2527,"prompt_tokens":995,"completion_tokens":1532,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":611,"completion_tokens_details":{"reasoning_tokens":1451}},"tokens_in":611,"tokens_out":1532,"duration_ms":11127,"temperature":1.0,"reasoning_tokens":1451,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-16T11:42:51.879647+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run SflLLM on a different downstream task, or with a non-IID client data split, after fitting $E(r)$ on E2E; then record the actual number of global rounds to the target loss for each candidate rank. If the observed $E(r)$ curve differs enough to change which rank minimizes total training delay, the reported latency reductions do not generalize.","supporting_citations":[{"cited_title":"Accelerating split federated learning over wireless communication networks,","cited_arxiv_id":null,"evidence_quote":"Joint split-point and bandwidth allocation baseline that the resource allocation scheme generalizes to LLM fine-tuning with LoRA."}],"review_version":1}